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Journal articles on the topic 'Automatic obstacle detection'
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Savitha, AC, Kumar KM Madhu, V. Prathap, et al. "Automatic Detection of Obstacle in Railway Track." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 1–5. https://doi.org/10.5281/zenodo.15385583.
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The main aim of the work is to enhance the safety and efficiency of train operations. The system focuses on detecting obstacles on the railway track and automatically applying the train's brakes to prevent accidents. With the growing concerns over train collisions and safety incidents, the introduction of an automated system that can detect obstructions and take immediate corrective actions is crucial. The proposed work utilizes sensors like ultrasonic sensors, cameras, and advanced algorithms for obstacle detection, coupled with automated braking mechanisms.
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Patil, Dr Nitin S., Jagruti J. Pande, Rachna P. Borse, Rinku M. Kamble, and Pratik A. Niphade. "Automatic Headlamp Control and Obstacle Detection." Journal of Energy Engineering and Thermodynamics, no. 34 (June 15, 2023): 9–15. http://dx.doi.org/10.55529/jeet.34.9.15.
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The numberof vehicles nowadaysincreasingvery fastly and so is technology. Safety is averyprime factor and has to be considered. vehicles are fitted with lots of safety features. Automatic Upper Dipper beam control and obstacle detection also need to be installed to improve safety to the next level. Thesefeatures are mainly used in nighttime driving or foggy weather or rain. Human eyes are very sensitive to light. when the light suddenly comes in contact with the eyes after darkness, the cornea present in the eyes gets contracted. i.e., human eyes will not be able to see for a short time. it is also called light glare. a vehicle coming from the opposite side with headlight in upper mode causes blind to the driver’s eyes. Insuch cases,there are high chances of occurring an accident. So, an automatic mechanism for controlling the light upper or dipper has to be made todecrease the chances of accidents during nighttime driving. Also, sometimes the driver won’t be able to see any object in nighttime orfoggy or rainy seasons. so, a sensor-based system where obstacle detection is enabled may assist from falling into an event of a crash. these two features assure you a very safe and comfortable drive. The operatingprinciple working and design of PCB is explained in this paper
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Xu, Xixi, Meiqi Zhang, Hao Tang, Weiye Xu, Bowen Sun, and Zhu’an Zheng. "Low-Illumination Parking Scenario Detection Based on Image Adaptive Enhancement." World Electric Vehicle Journal 16, no. 6 (2025): 305. https://doi.org/10.3390/wevj16060305.
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Aiming at the problem of easily missed and misdetected parking spaces and obstacles in the automatic parking perception task under low-illumination conditions, this paper proposes a low-illumination parking space and obstacle detection algorithm based on image adaptive enhancement. The algorithm comprises an image adaptive enhancement module, which predicts adaptive parameters using CNN and integrates the low-light image enhancement via illumination map estimation and contrast-limited adaptive histogram equalization algorithms for image processing. The parking space and obstacle detection module adopts parking space corner detection based on image gradient matching, as well as obstacle detection utilizing yolov5s, whose feature pyramid network structure is optimized. The two modules are cascaded to optimize the prediction parameters of the image adaptive enhancement module, comprehensively considering the similarity loss of parking space corner matching and the obstacle detection loss. Experiments show that the algorithm makes the image pixel value distribution more balanced in low-light scenarios, the accuracy of parking space recognition reaches 95.46%, and the mean average precision of obstacle detection reaches 90.4%, which is better than the baseline algorithms, and is of great significance for the development of automatic parking sensing technology.
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Zhang, Qiang, Fei Yan, Weina Song, Rui Wang, and Gen Li. "Automatic Obstacle Detection Method for the Train Based on Deep Learning." Sustainability 15, no. 2 (2023): 1184. http://dx.doi.org/10.3390/su15021184.
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Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation.
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Ramesh, Dr V., K. Praveena, P. Venkata Lakshmi, A. Bhavana, and A. Mani Venkata Sai. "Automatic Fire Fighting Robot." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43641.
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Fire hazards pose significant threats to both life and property, necessitating rapid and efficient response mechanisms. This paper presents the design and implementation [1] of an autonomous Fire Fighting Robot capable of detecting and extinguishing fires with minimal human intervention. The system employs four fire sensors strategically placed to detect flames from multiple directions, ensuring comprehensive fire monitoring. An ultrasonic sensor enables obstacle detection and assists in navigation to prevent collisions while approaching the fire source [3]. The robot’s mobility is powered by BO motors controlled through a motor driver, while a servo motor precisely directs the fire suppression mechanism. A relay-controlled water pump is activated upon fire detection, enabling real-time fire fighting operations [5]. The system is built around a microcontroller, which processes sensor data and executes real-time decisions for fire suppression. While the robot primarily operates autonomously [2], it also features a remote control mode for enhanced flexibility in complex fire scenarios. This autonomous fire fighting system [2] demonstrates a cost-effective and efficient approach to mitigating fire hazards, making it suitable for industrial, residential, and hazardous environments. Keywords- Fire Fighting Robot, Autonomous Fire Suppression, Fire Detection, Microcontroller, Obstacle Avoidance, and Remote Control.
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Liu, Biao, Bihao Tian, and Junchao Qiao. "Mine track obstacle detection method based on information fusion." Journal of Physics: Conference Series 2229, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2229/1/012023.
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Abstract As an important part of the coal mine transportation system, coal mine underground rail transportation undertakes the core transportation task of coal mine underground. Its safe and efficient operation is directly related to the efficiency of coal mine production and transportation. In view of this, this paper proposes a mine track environmental obstacle detection system which integrates camera and Lidar information to realize real-time automatic detection of obstacles in front of the underground track mine cart. The specific research results are as follows: first of all, a point cloud clustering algorithm for the mine environment is designed to extract the obstacle information, and then the YOLOv5 algorithm is used to identify the obstacle information in the image. Finally obstacle information from image and point cloud are fused at the decision-level. The obstacle detection method proposed in this paper can be successfully identified to meet the requirements of the obstacle object detection function of the rail vehicle.
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Ramesh, Patil, Bapure Diana, Hasgonda Manjula, Jatnor Monika, and Metkar Monika. "Autonomous Obstacle Detection, Automatic Stopping, and Alert System for Train." Research and Review: VLSI Design, Tools and It's Application 1, no. 2 (2025): 8–12. https://doi.org/10.5281/zenodo.15387083.
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<em>The development of smart transportation systems has revolutionized rail networks, improving safety, efficiency, and automation. This project aims to design an "Autonomous Obstacle Detection, Automatic Stopping, and Alert System for Smart Trains Using Arduino Uno." The system incorporates an Arduino Uno microcontroller, I2C LCD, ultrasonic sensors, motor driver, DC motors, RF (Radio Frequency) transmitter and receiver modules, LEDs, and a buzzer. The core objective is to ensure real-time detection of obstacles on railway tracks, triggering automatic stopping of the train to avoid collisions and generating alerts to notify authorities or operators. The system uses ultrasonic sensors to measure the distance to potential obstacles, and if a critical threshold is breached, the motor driving the train is halted using the motor driver.</em>
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Dr.K.Venugopal, Rao, Akhila P., Madhu G., Navya P., and Manoj M. "Design and Implementation of Automatic Engine Off and Theft Detection of Vehicle." Design and Implementation of Automatic Engine Off and Theft Detection of Vehicle 8, no. 12 (2023): 8. https://doi.org/10.5281/zenodo.10403903.
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In recent times property theft has become a major concern. Vechile theft is at the top of the list and happens frequently everywhere in the world. several technologies are developing in response to this problem, and new approaches are being created to find a solution. Since burglars are now aware of the techniques used in vehicle theft detection, they attempt to compromise the system in order to take the vehicle. This paper proposes to enhance vehicle safety by integrating alcohol detection, obstacle avoidance, and theft detection systems. The alcohol detection system ensures responsible driving by monitoring the driver's alcohol level, while the obstacle avoidance mechanism uses sensors to detect obstacles and navigate the vehicle accordingly. Additionally, a theft detection system employs sensors to identify unauthorized access. Upon detection of alcohol influence or potential theft, the system triggers an automatic engine shutdown and activates a buzzer for alerting authorities, promoting safer driving practices and enhancing vehicle security. Keywords:- Vehicle Theft, GSM, Arduino, Sensors.
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Han, Weiliang, Shixuan Leng, Lisha Ma, Bo Gao, and Xuping Wu. "Automatic Obstacle Avoidance Robot based on Artificial Intelligence Detection and Recognition." Frontiers in Science and Engineering 3, no. 4 (2023): 27–31. http://dx.doi.org/10.54691/fse.v3i4.4771.
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This paper designs an automatic obstacle avoidance robot based on artificial intelligence detection and recognition, including a shell, and a detection and obstacle avoidance mechanism is set inside the shell; the detection and obstacle avoidance mechanism includes a movable member, a screw, a first electric actuator, a first camera, a gear, a rack and a steering assembly; by setting the detection and obstacle avoidance mechanism, the first camera and the second camera can take pictures of the surrounding environment during the movement of the shell, and when the micro When the computer recognizes the obstacle, it controls the servo motor to run and makes the screw rotate, so that the movable part moves in the direction of no obstacle, and the rack at the bottom of the movable part moves together during the moving process of the movable part, and the gear will be driven to rotate the steering wheel during the moving process of the rack, so that the robot can perform obstacle avoidance operation, and the height of the camera can also be adjusted by adjusting the first electric pusher and the second electric pusher to adjust the By adjusting the first and second electric actuators, the height of the camera can be adjusted to adjust the detection range, making the robot more flexible.
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NISHINA, Takumi, Etsuro SHIMIZU, Tsuyoshi ODE, and Ayako UMEDA. "Obstacle Detection System for Automatic Ship at Night Navigation." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (2019): 2A1—H06. http://dx.doi.org/10.1299/jsmermd.2019.2a1-h06.
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Patil, Mr Yuvraj. "PLC BASED RAILWAY ACCIDENT AVOIDANCE SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31894.
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The paper presents a comprehensive framework for enhancing railroad security by integrating technologies such as signalling arrangements, train closure security systems, automatic railroad gate control, intrusion detection, obstacle detection, tunnel control, and automatic blockade systems. These systems utilize sensors and advanced controls to detect and mitigate potential threats, enhancing security measures and reducing the risk of accidents. Programmable Logic Controllers (PLCs) in railroad systems manage several security protocols, including emergency brake, over-speed control, train door control, and fire detection. They also ensure passenger and train safety by monitoring temperature, pressure, and voltage parameters. In the event of any anomalies, the PLC triggers an alert and takes necessary actions to prevent accidents. Key Words: : Automatic Barricade System, Automatic Railway Gate Controlling, Crack Detection System, Obstacle Detection, PLC, Signalling Deployment System, Tunnel Power Saving System.
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Gao, Chenhao. "Obstacle Detection Technology for Autonomous Driving Based on Deep Learning." Transactions on Computer Science and Intelligent Systems Research 3 (April 10, 2024): 117–22. http://dx.doi.org/10.62051/c3evm786.
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With the rapid growth of artificial intelligence (AI) technology, traditional obstacle detection equipment faces multiple challenges such as high cost, low real-time performance, non normalization, dependence on manual operation, and time-consuming and labor-intensive. To address these shortcomings, this article proposes a deep learning (DL) based obstacle detection technology for autonomous driving on the road surface. As a complex system that integrates multiple key components such as environmental perception, positioning and navigation, path planning, and motion control, one of the core technologies of autonomous vehicles is accurate perception of the surrounding environment. In practical applications, autonomous vehicles often face complex and variable road environments, which may lead to a decrease in the quality of images captured by cameras, resulting in blurry and unclear phenomena. The DL method, especially the object detection algorithm, has shown unique advantages in visual perception and recognition in autonomous driving scenes. This paper deeply studies the obstacle detection technology of automatic driving road based on DL, aiming to achieve efficient and accurate obstacle recognition, improve the safety and reliability of auto drive system, and promote the further growth of automatic driving technology.
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Nguyen, Phuc Thanh-Thien, Toan-Khoa Nguyen, Dai-Dong Nguyen, Shun-Feng Su, and Chung-Hsien Kuo. "Road Anomaly Detection with Unknown Scenes Using DifferNet-Based Automatic Labeling Segmentation." Inventions 9, no. 4 (2024): 69. http://dx.doi.org/10.3390/inventions9040069.
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Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data’s preparation and processing time-consuming and labor-intensive. To address this challenge, this study introduces a transfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework utilizes a pretrained attention-based network, DifferNet, to efficiently perform semantic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior knowledge from the Cityscapes dataset to identify high-entropy areas as road obstacles by analyzing differences between the input and resynthesized images. The resulting road anomaly map was refined using depth information to produce a robust drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the ALS framework, with validation conducted on the GMRPD dataset. Experimental results demonstrated that the proposed ALS framework achieved mean precision, mean recall, and mean intersection over union (IoU) rates of 80.31%, 84.42%, and 71.99%, respectively. The ALS framework, through the use of transfer learning and the DifferNet network, offers an efficient solution for semantic segmentation of new, unlabeled datasets, underscoring its potential for improving obstacle avoidance in autonomous mobile robots.
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Wu, Fei, and Yueyang Zhao. "Non-contact automatic COVID-19 nucleic acid detection robot." Applied and Computational Engineering 5, no. 1 (2023): 848–52. http://dx.doi.org/10.54254/2755-2721/5/20230726.
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This product is mainly used to realise the contactless automatic COVID-19 nucleic acid detection robot, which can navigate to the workstation or location of the set personnel through the camera and radar and is mainly applied to the autonomous movement function of the existing automatic nucleic acid detection robot. The robot will conduct intelligent navigation according to the position of the nucleic acid person, avoid obstacles autonomously through radar and camera, and use clever navigation to plan the nearest route to the position part of the needed nucleic acid. This product carries a camera and laser radar based on SLAM technology for obstacle avoidance of autonomous mobile robots, through the experiment of advanced algorithms and the team constantly optimised. The algorithm makes its universality, compatibility, real-time and precision to achieve the desired effect. These autonomous mobile robots in the industrial production industry are essential to the application.
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Xu, Yi, Shanshang Gao, Guoxin Jiang, et al. "Parking Space Detection and Path Planning Based on VIDAR." Journal of Robotics 2021 (December 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/4943316.
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The existing automatic parking algorithms often neglect the unknown obstacles in the parking environment, which causes a hidden danger to the safety of the automatic parking system. Therefore, this paper proposes parking space detection and path planning based on the VIDAR method (vision-IMU-based detection and range method) to solve the problem. In the parking space detection stage, the generalized obstacles are detected based on VIDAR to determine the obstacle areas, and then parking lines are detected by the Hough transform to determine the empty parking space. Compared with the parking detection method based on YOLO v5, the experimental results demonstrate that the proposed method has higher accuracy in complex parking environments with unknown obstacles. In the path planning stage, the path optimization algorithm of the A ∗ algorithm combined with the Bezier curve is used to generate smooth curves, and the environmental information is updated in real time based on VIDAR. The simulation results show that the method can make the vehicle efficiently avoid the obstacles and generate a smooth path in a dynamic parking environment, which can well meet the safety and stationarity of the parking requirements.
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Mishra, Anshika. "BLUETOOTH BASED AUTOMATIC VACUUM CLEANER." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03045.
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Abstract: This project presents the design and implementation of a Bluetooth-controlled automatic vacuum cleaner. The system integrates a microcontroller with Bluetooth connectivity, enabling users to control the vacuum cleaner via a smartphone application. The cleaner utilizes sensors for obstacle detection and mapping, ensuring efficient cleaning across various surfaces. The Bluetooth interface allows for remote control, start/stop functions, and customizable cleaning patterns, enhancing user convenience. The system is designed to optimize battery usage and improve cleaning efficiency with minimal human intervention. This innovation combines automation, wireless technology, and smart functionality, offering a user-friendly solution for home cleaning tasks. Keywords: automatic vacuum cleaner, robotic vacuum, sensors, navigation, obstacle avoidance, cleaning efficiency, mapping, home automation, autonomous cleaning, scheduling, etc.
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Okamoto, Yutaro, Chinthaka Premachandra, and Kiyotaka Kato. "A Study on Computational Time Reduction of Road Obstacle Detection by Parallel Image Processor." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 5 (2014): 849–55. http://dx.doi.org/10.20965/jaciii.2014.p0849.
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Automatic road obstacle detection is one of the significant problem in Intelligent Transport Systems (ITS). Many studies have been conducted for this interesting problem by using on-vehicle cameras. However, those methods still needs a dozens ofmillisecondsfor image processing. To develop the quick obstacle avoidance devices for vehicles, further computational time reduction is expected. Furthermore, regarding the applications, compact hardware is also expected for implementation. Thus, we study on computational time reduction of the road obstacle detection by using a small-type parallel image processor. Here, computational time is reduced by developing an obstacle detection algorithm which is appropriated to parallel processing concept of that hardware. According to the experimental evaluation of the new proposal, we could limit computational time for eleven milliseconds with a good obstacle detection performance.
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Dhawan, Ajay P. "Automatic Braking System using Ultrasonic wave." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4757–61. http://dx.doi.org/10.22214/ijraset.2022.45055.
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Abstract: An automatic Braking system is an intelligent mechatronic system includes an Ultrasonic wave emitter provided on the front portion of a car producing and emitting Ultrasonic waves. An Ultrasonic receiver is also placed on the front portion of the car operatively receiving a reflective Ultrasonic wave signal. The reflected wave (detected pulse) gives the distance between the obstacle and the vehicle. Then a microcontroller is used to control the speed of the vehicle based on the detection pulse information to push the brake pedal and apply brake to the car stupendously for safety purpose. Automotive vehicles are increasingly being equipped with collision avoidance and warning systems for predicting the potential collision with an external object, such as another vehicle or a pedestrian. Upon detecting a potential collision, such systems typically initiate an action to avoid the collision and/or provide a warning to the vehicle operator. The aim is to design and develop a control system based on an automatic, intelligent and electronically controlled automotive braking system for automobiles is called as “Sensor based Electromagnetic Braking System”. This Braking system consists of IR transmitter and receiver circuit and the vehicle. The IR sensor is used to detect the obstacle. There is any obstacle in the path, the IR sensor senses the obstacle and giving the control signal to the microcontroller, which in turn sends a signal to the motor to stop and also to the Electromagnet so as to stop the vehicle as programmed. This project facilitates electromagnetic braking system using solenoid. Here in fabrication module include a vehicle prototype frame associated with a dc motor and a electromagnet.
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Kiran ,, Prof N. "Automatic Fire-Fighting Robot Using Arduino." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45129.
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Abstract: The “Automatic Fire-Fighting Robot” utilizing the Arduino Uno is an innovative safety automation project designed to detect and extinguish small-scale fires autonomously. This system focuses on rapid fire detection, accurate navigation, and effective fire suppression using simple, cost-effective, and scalable technology. The robot is built on the Arduino Uno microcontroller and integrates components such as flame sensors, ultrasonic sensors, servo motors, water/sprinkler pumps, and motor driver modules. Upon detecting a fire, the robot automatically moves toward the flame and activates its extinguishing mechanism. This project is aimed at reducing fire-related risks, particularly in domestic, laboratory, and industrial settings. Future developments may include thermal imaging, remote monitoring, and enhanced fire suppression techniques. Index Terms: Fire detection, Arduino robot, flame sensor, autonomous fire-fighting, ultrasonic navigation, firefighting robot, fire suppression system, robotic safety, obstacle avoidance, smart automation.
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Lin, Jung-Shan, Hao-Jheng Wu, and Jeih-Weih Hung. "Automatic Parallel Parking System Design with Fuzzy Control and LiDAR Detection." Electronics 14, no. 13 (2025): 2520. https://doi.org/10.3390/electronics14132520.
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This paper presents a self-driving system for automatic parallel parking, integrating obstacle avoidance for enhanced safety. The vehicle platform employs three primary sensors—a web camera, a Zed depth camera, and LiDAR—to perceive its surroundings, including sidewalks and potential obstacles. By processing camera and LiDAR data, the system determines the vehicle’s position and assesses parking space availability, with LiDAR also aiding in malfunction detection. The system operates in three stages: parking space identification, path planning using geometric circles, and fine-tuning with fuzzy control if misalignment is detected. Experimental results, evaluated visually in a model-scale setup, confirm the system’s ability to achieve smooth and reliable parallel parking maneuvers. Quantitative performance metrics, such as precise parking accuracy or total execution time, were not recorded in this study but will be included in future work to further support the system’s effectiveness.
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Annasaheb, Devgire Shubham. "Automatic Floor Cleaning Robot." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42829.
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Floor cleaning robots have emerged as an innovative solution to simplify the process of maintaining cleanliness in domestic and commercial environments. These autonomous robots utilize a combination of sensors, algorithms, and mechanical systems to navigate spaces, detect obstacles, and perform cleaning tasks efficiently. This paper explores the various technologies behind floor cleaning robots, including sensor fusion, path planning algorithms, and autonomous decision-making mechanisms. We also examine the key factors affecting their performance, such as battery life, cleaning efficiency, and adaptability to different types of floor surfaces. Additionally, the paper evaluates the environmental and economic impacts of these robots, highlighting their potential to reduce human labor and energy consumption. Future advancements, including AI integration, multi-surface adaptability, and enhanced user interaction, are also discussed, along with the challenges that remain in improving their performance in complex real-world environments. The findings of this paper provide insights into the ongoing development of floor cleaning robots and their evolving role in smart home ecosystems. Keywords- Automatic Floor Cleaning, ESP32, Ultrasonic Sensor, Obstacle Detection, IoT, Robotics. .
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Anshad and Marrynal S. Eastaff Mrs. "LINE FOLLOWING AND OBSTACLE DETECTION ROBOT." Volume 7 Issue 10 7, no. 10 (2021): 1–3. https://doi.org/10.5281/zenodo.5675444.
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Line follower is a smart autonomous robot that detects or follows a visible line embedded in the ground. The trail is predetermined and can be selected with a high contrast color or with a black line visible on the trail surface. Infrared sensors are used to detect these lines. The robot movement is automatic and can be used for applications of long distances. It is the fundamental line follower robot’s function. The device proposed for commercial, medical, rescue and military operations are extremely useful. In particular, these past constraints are no longer necessary with recent technological advances in computing. The production of tracking systems can now be made more capable of reliably estimating the target location behind the obstacle. The benefit of these technologies consists in the possibility of using an ultrasonic method for measurement without direct contact with a target. Different models and systems for indoor and outdoor object detection have been described in the literature.Using optical, heat base, infrared and ultrasonic approaches, object localization techniques were introduced. Indoor positioning systems monitor and locate objects and enclose environments inside buildings. Wireless methods, optical tracking, and ultrasonic techniques are used for object position detection systems. The goal of this study is to develop a monitoring system that follows certain paths and can detect objects and edges using ultrasonic frequencies. If some object is put, a regular line follower will try to move and smash the obstacle. This prototype of line follower robot tries to push the limit little to overcome this issue. It has been built in a way that any obstacle in front of it can be identified. It will stop and will not pass until the barrier remains. Also, it is able to identify every front edge and comply similarly. In industries, such as material handling, this type of robot performs many tasks. These robots are also used as machine-controlled carrier instruments in old conveyor belts switching industries
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Masomera, Felix Gerald, Confidence Z. Gweera, and John Batani. "Automatic Home Floor Cleaner Robot." International Journal of Electronics, Communications, and Measurement Engineering 9, no. 2 (2020): 1–16. http://dx.doi.org/10.4018/ijecme.2020070101.
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Due to the presence of semi-automated home floor cleaner systems that are unable to do both mopping and draining of water on floor surfaces, this research investigates the challenges being faced by the elderly and the differently-abled people in using these cleaner systems and develops a robot that can effectively clean floor edges and corners. In attaining an automatic home floor cleaner robot, this research utilizes the vector field histogram (VFH) and path planning algorithms to realize automatic obstacle detection, edge detection, and moderate floor coverage when mopping. The effectiveness of algorithms depends on floor edges, floor corners, and water presence on the floor. The results enjoy the satisfaction of the elderly, the differently abled, and other individuals who are too busy to do the work since they no longer have to intervene in the cleaning process except to just power on the cleaner robot. However, the robot requires continuous charging of the battery, and it does not work on carpeted floors.
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Lan, Jinhui, Yaoliang Jiang, Guoliang Fan, Dongyang Yu, and Qi Zhang. "Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic." Journal of Signal Processing Systems 82, no. 3 (2015): 357–71. http://dx.doi.org/10.1007/s11265-015-1006-4.
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Karmakar, Bishal. "Violence Resisting Transforming Robot with Ability of Obstacle Detection." European Journal of Engineering and Technology Research 1, no. 6 (2018): 44–48. http://dx.doi.org/10.24018/ejeng.2016.1.6.230.
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This paper presents the design; Mechanism and implementation of Automatic Law enforce Transforming Robot with the ability to resist violence automatically or manually as instructed by the operator. The robot also contains a specialty of detection of protesters by sonar technique before activating water thrower system. The robot consists of two hands and one of those is carrying water thrower system. Any kind of anti-violence devices can be added in these two hands. With this, any violence can be cool down or any internal attack can be turned down which eventually is hard and dangerous for law enforce officers. For its transforming structure, it can easily be transported, helpful to use in both mode and can be addressed as non-threatening design for public. Arduino Uno performs as the brain that controls the whole body structure.
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Chen, Tian Ding. "Fast Computing Scheme for AGV Obstacle Distance Measure and Road Recognition." Advanced Materials Research 108-111 (May 2010): 500–506. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.500.
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Automatic obstacle avoidance and road detection for the Automation Guiding Vehicle (AGV) need to calculate the distance, object shape parameter. This paper presents a new obstacle distance calculating method based on monocular vision. Through scene in the two different images corresponding feature points are accurately matched, according two different video frame images disparity to compute distance between AGV and obstacle. In order to accurately find feature points, this paper uses a detection algorithm based on Harris corner, combines epipolar constraint and disparity gradient for image matching. These steps accelerate measure computing results. The basis of known structural characteristics of the road presents a road image morphology algorithm to filter road image noise, combines fast threshold algorithm to achieve a set of structured road recognition guiding system. Experimental results show that the detection method can correctly recognize the structured road of interference with certain obstacle, and achieve a visual robot guiding system.
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Xu, Xiao Wen, and Jin Yi He. "Design of Intelligent Guide Vehicle for Blind People." Applied Mechanics and Materials 268-270 (December 2012): 1490–93. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1490.
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Traditionally, guide dogs are used as a travel aid for blind people. But they cannot be popularized because the training cost for guide dogs is quite high. This paper presents an intelligent guide vehicle prototype for blind people. The system is developed by using ARM as a controller and processor, via integration of ultrasonic detection and photoelectric detection technology, aiming at automatically navigation for blind people. This system mainly consists of four modules, which are ultrasonic detection module, photoelectric detection module, voice prompt module and automatic control module. The signal reflected by roadblock on the road can be detected by ultrasonic detection module which can work out distance between roadblock and guide vehicle. The aim of photoelectric detection module is to realize the function of road recognition and tracking. Voice prompt module can provide information of distance of roadblock. Strategies of obstacle avoidance were taken to the blind when the guide vehicle is going to avoid roadblock. If the roadblock is less than 50cm away, the automatic control module will automatically set the guide vehicle to avoid obstacles. Preliminary experimental results show that users can control and navigate with the intelligent guide vehicle.
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Mohamed, Mausoom, Gnana Jeba Das Dasaian, and Udhuma Abdul Latheef. "Sensor-Based Motorcycle Restrain System with an Integrated Automatic Brake System." Applied Mechanics and Materials 926 (April 23, 2025): 87–97. https://doi.org/10.4028/p-1qrm13.
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This article focuses on the development and testing of a sensor-based motorcycle restraint system equipped with an automated braking mechanism. The system integrates ultrasonic sensors, a DC linear motor, and an Arduino microcontroller to enhance motorcycle safety by detecting obstacles and applying brakes automatically. A prototype was constructed using a wooden frame and bicycle wheels to emulate the motorcycle's dynamics. The braking system utilized a genuine motorcycle drum brake, and the DC linear motor was linked to activate the brake lever based on sensor feedback. LED strips and a buzzer provided visual and audible warnings, indicating the presence and proximity of obstacles. The system was tested under various conditions to evaluate its responsiveness, accuracy, and dependability. The findings revealed that the ultrasonic sensors provided precise distance measurements, allowing for a timely response in obstacle detection scenarios. The automatic braking mechanism demonstrated a 65% reduction in reaction time compared to manual braking, improving rider safety significantly. The system also managed to reduce the braking distance at speeds of 50 km/h, demonstrating its efficacy in emergency situations. Data collected during prototype testing indicated that the system effectively engaged the brake within a 10 cm detection range, issued appropriate warnings, and accurately monitored brake wear. Despite some limitations, such as environmental sensitivity and the prototype's use of simplified materials, the research underscores the system's potential to enhance motorcycle safety. Future recommendations include improving sensor reliability in adverse weather conditions, upgrading the prototype to mimic full-scale motorcycle dynamics, and incorporating additional features like traction control and rider-assist technology.
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Kyaw, Kyaw Lin. "Arduino Based Automatic Car Washing System." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2074–76. https://doi.org/10.5281/zenodo.3591659.
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In this modern era automation helps us to save time, cost as well as manpower. Vehicles are used extensively for transportation. To clean these vehicles there is a need of a proper washing system. The most common problems often encountered while cleaning these vehicles are consumption of water, manpower and time. The easy and effective systems for maintaining the vehicles cleanliness become high demand. Therefore, this paper focuses on car washing system using ARDUINO. The exterior of the car will be washed by detecting the car on conveyor belt and the processes are controlled by ARDUINO. The sensors are placed on conveyor belt at different places for car detection, washing, cleaning and drying. This system can be applied in car manufacturing companies after final assembly of car, service stations, car replacing and maintaining stations, and car body building industry. Kyaw Kyaw Lin "Arduino Based Automatic Car Washing System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27817.pdf
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Liu, Y. Y., Z. F. Chi, Q. X. Hu, Y. Yao, and J. Huang. "Vacuum Casting Bubble Automatic Elimination System Based on on-Line Machine Vision Detection." Advanced Materials Research 670 (March 2013): 222–27. http://dx.doi.org/10.4028/www.scientific.net/amr.670.222.
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Bubbles in vacuum casting process are dense, tiny and overlapped. The difference between bubbles and the background is so indistinct that makes bubble detection a great difficulty. Bubble elimination has become the main obstacle in the way of vacuum casting machine’s automation development. Bubble automatic detection is the base of bubble elimination. A machine vision bubble on-line detecting and eliminating platform is constructed, using industrial high-speed CCD camera and professional LED illumination. According to the features of vacuum casting bubbles, the edge pixel ratio algorithm is designed especially for vacuum casting bubble detection. The algorithm is realized using VC++ and Open CV. The integrated system proposed can detect and eliminate vacuum casting bubbles on-line automatically. As it’s confirmed, there is an obviously positive correlation between the edge pixel ratio and the bubble denseness. This detection system makes sense in vacuum casting bubble elimination.
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Karoui, Imen, Isabelle Quidu, and Michel Legris. "Automatic Sea-Surface Obstacle Detection and Tracking in Forward-Looking Sonar Image Sequences." IEEE Transactions on Geoscience and Remote Sensing 53, no. 8 (2015): 4661–69. http://dx.doi.org/10.1109/tgrs.2015.2405672.
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SHANKAR, R. BHANU SATYA. "Object Detection in Automatic Cars Using YOLO Model." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51169.
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Object detection plays a vital role in enabling autonomous vehicles to perceive and interpret their surroundings accurately and in real time. This paper presents the application of the YOLO (You Only Look Once) object detection algorithm within the perception system of self-driving cars. YOLO is a deep learning- based approach that performs object detection as a single regression problem, allowing for high-speed and accurate identification of multiple objects, such as vehicles, pedestrians, traffic lights, and road signs, in a single frame. By integrating YOLO into the vehicle’s vision system, the autonomous system can make timely decisions to ensure safe navigation and obstacle avoidance. This study discusses the architecture of YOLO, its real-time performance capabilities, and its effectiveness in dynamic driving environments. Experimental results demonstrate that YOLO provides a practical solution for real-time object detection in autonomous driving systems, offering a balanced trade-off between speed and accuracy necessary for safe and efficient operation.
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Xiao, Tianwen, Yongneng Xu, and Huimin Yu. "Research on Obstacle Detection Method of Urban Rail Transit Based on Multisensor Technology." Journal of Artificial Intelligence and Technology 1, no. 1 (2021): 61–67. http://dx.doi.org/10.37965/jait.2020.0027.
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With the rapid development of urban rail transit, passenger traffic is increasing, and obstacle violations are more frequent, and the safety of train operation under high-density traffic conditions is becoming more and more thought-provoking. In order to monitor the train operating environment in real time, this paper first adopts multi-sensing technology based on machine vision and lidar, which is used to collect video images and ranging data of the track area in real time, and then it performs image preprocessing and division of regions of interest on the collected video. Then, the obstacles in the region of interest are detected to obtain the geometric characteristics and position information of the obstacles. Finally, according to the danger level of the obstacles, determine the degree of impact on train operation , the automatic response mode and manual response mode of the signal system are used to transmit the detection results to the corresponding train to control train operation. Through simulation analysis and experimental verification, the detection accuracy and control performance of the detection method are confirmed, which provides safety guarantee for the train operation.
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Yan, Fei, Yiran Gu, and Yunlai Sun. "Deep Learning-Based Train Obstacle Detection Technology: Application and Testing in Metros." Electronics 14, no. 7 (2025): 1318. https://doi.org/10.3390/electronics14071318.
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With the rapid development of urban rail transit, unmanned train driving technology is also advancing rapidly. Automatic obstacle detection is particularly crucial and plays a vital role in ensuring train operation safety. This paper focuses on train obstacle detection technology and testing methods. First, we review existing obstacle detection systems and their testing methods, analyzing their technical principles, application status, advantages, and limitations. In the experimental section, the Intelligent Train Eye (ITE) system is used as a case study. Black-box testing is conducted in the level high-precision (LH) mode, with corresponding test cases designed based on various scenarios that may arise during train operations. White-box testing is performed in the level exploration (LE) mode, where the test results are meticulously recorded and analyzed. The test cases in different modes comprehensively cover the testing requirements for train operations. The results indicate that the ITE system successfully passes most of the test cases and meets the primary functional requirements.
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Shang, Yehua, Hao Wang, Wuchang Qin, et al. "Design and Test of Obstacle Detection and Harvester Pre-Collision System Based on 2D Lidar." Agronomy 13, no. 2 (2023): 388. http://dx.doi.org/10.3390/agronomy13020388.
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Aiming at the need to prevent agricultural machinery from colliding with obstacles in the operation of unmanned agricultural machinery, an obstacle detection algorithm using 2D lidar was proposed, and a pre-collision system was designed using this algorithm, which was tested on a harvester. The method uses the differences between lidar data frames to calculate the collision times between the farm machinery and the obstacles. The algorithm consists of the following steps: pre-processing to determine the region of interest, median filtering, and DBSCAN (density-based spatial clustering of applications with noise) to identify the obstacle and calculate of the collision time according to the 6σ principle. Based on this algorithm, a pre-collision system was developed and integrated with agricultural machinery navigation software. The harvester was refitted electronically, and the system was tested on a harvester. The results showed that the system had an average accuracy rate of 96.67% and an average recall rate of 97.14% for being able to stop safely for obstacles in the area of interest, with a summed average of 97% for both the accuracy and recall rates. The system can be used for an emergency stop when encountering obstacles in the automatic driving of agricultural machinery and provides a basis for the unmanned driving of agricultural machinery in more complex scenarios.
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Sharma, Sachin, and Dharmesh Shah. "Real-Time Automatic Obstacle Detection and Alert System for Driver Assistance on Indian Roads." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 3 (2016): 635. http://dx.doi.org/10.11591/ijeecs.v1.i3.pp635-646.
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Road crashes have been a major problem in India in recent times. The occurrences have increased considerably owing to the influx of four-wheelers and two-wheelers. The interior roads connecting villages and towns have been instrumental in multiple animal-vehicle collisions. Although the figure is not too large compared to other causes of road-related injuries, they are significant in number. Though numerous efforts have been in progress to solve and reduce the number of collisions, lack of practical applications and resources along with quality analytical data (for training and testing) related to animal-vehicle collision has impeded any major breakthrough in the scenario. In our current work, we have proposed and designed a system based on histogram research including oriented gradients and boosted cascade classifiers for automatic cow detection. The method is implemented in Opencv software and tested on various video clips involving cow movements in various scenarios. The proposed system has achieved an overall efficiency of 80% in terms of cow detection. The proposed system is a low-cost, highly reliable system which can easily be implemented in automobiles for detection of cow or any other animal after proper training and testing on the highway.
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V, Ramesh Babu, Neelakanteshwar Rao B, and Veeresham K. "Multifunctional Autonomous Robot." Advancement and Research in Instrumentation Engineering 6, no. 3 (2023): 31–39. https://doi.org/10.5281/zenodo.10200081.
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<i>The goal of the work is to create a prototype of Multi-functional Robotic Vehicle using Arduino UNO that can be controlled through voice, Bluetooth, obstacle avoidance and path following modes. The switching over between these modes is achieved conveniently through a mobile application. Hence, it reduces the need of physical contact and automates the vehicle. The prototype includes various components such as a Bluetooth module, IR sensor, and Ultrasonic sensor, Servo motor and motor driver. The paper presents an Arduino-based vehicle control system that relies on voice commands through Google Assistant and eliminates manual operation. The system also includes an automatic obstacle detection system. The robot could be useful in several industries, reducing the need for human labor and improving safety in risky situations. </i>
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Zhang, Ying, Pu Qiong Yang, Feng Wei Yuan, and Yu Rong Hu. "Design of the Advanced Automatic Door Controller Based on PLC." Advanced Materials Research 591-593 (November 2012): 1338–41. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1338.
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In order to enhance the reliability of the automatic door operation, S7-200 programmable logic controller (PLC) was used as the core of the automatic door system. The principle of control system was analyzed with description of the control system hardware. It was introduced that the working process of control circuits about human body detection sensor and automatic door position detection and obstacle detection and so on. It was introduced how to choose a PLC type, to really settle the I/O orders. Combining the operation characteristics of programmable controller, a reasonable optimization was made for the work flow of the system. On this basis, using structured programming method, the program flow charts of main program module was developed. The debugging of the control system was developed on simulation software. The practical application shows that the control system was good and reliable.
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Zhao, Ying, Yongxiang Sui, Jinsen Hou, Qun Sun, and Chong Wang. "Design of a Vehicle Chassis Inspection Robot Based on WiFi Network." Open Electrical & Electronic Engineering Journal 11, no. 1 (2017): 154–64. http://dx.doi.org/10.2174/1874129001711010154.
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Background: The vehicle chassis inspection robot introduced in this paper is capable of realizing automatic tracking, obstacle avoidance, and video image acquisition. The robot can be connected to a computer, mobile phone or other terminals through a WiFi network built within the robot, so as to achieve real time control of the robot motion, and to display videos or images collected by the robot on the computer screen. The system is simple and easy to operate, with high stability, high flexibility, precise directional control, and can satisfy the requirements in harsh environment. Methods and Materials: This design adopts a STC11F32XE microcontroller as the core, uses an ultrasonic sensor to detect the road objects and calculate the distance to the objects, anticipates and avoids obstacle during processing. The camera performs image acquisition and returns the picture to help easy detection of automotive chassis and manual robot control. The robot uses an infrared sensor to realize automatic obstacle avoidance, and it controls the travel speed as well as automatic stop by changing the PWM duty cycle. Conclusion: Through this research, an intelligent vehicle parking inspection system has been developed.
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Song, Zhen, and Hongwei Deng. "Research on Sensor Optimization Technology of Driverless Vehicle." Frontiers in Computing and Intelligent Systems 4, no. 2 (2023): 131–37. http://dx.doi.org/10.54097/fcis.v4i2.10370.
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Driverless cars in operation, the perception of the surrounding environment demand is very rich, it is a kind of automatic detection of road information, detection of obstacles, calculation of obstacle location, speed and other functions, but due to the limitations of technology and detection means, the perception data of self-driving cars is not accurate enough, prone to safety accidents. Therefore, the optimization of various sensors can greatly improve the safety performance of unmanned vehicles, thereby greatly promoting the development of unmanned technology. Environmental perception technology is one of the core technologies of unmanned cars, environmental perception information comprehensiveness and accuracy is the guarantee of safe driving of unmanned cars, this paper elaborates on image recognition, sensor layout, sensor perception range and accuracy, sensor anti-interference ability and rapid processing of sensor massive data in environmental perception technology.
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Sharma, Sachin, and Dharmesh Shah. "Real-time automatic obstacle detection and alert system for driver assistance on Indian roads." International Journal of Vehicle Autonomous Systems 13, no. 3 (2017): 189. http://dx.doi.org/10.1504/ijvas.2017.083499.
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Shah, Dharmesh, and Sachin Sharma. "Real-time automatic obstacle detection and alert system for driver assistance on Indian roads." International Journal of Vehicle Autonomous Systems 13, no. 3 (2017): 189. http://dx.doi.org/10.1504/ijvas.2017.10004233.
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YAMAMOTO, Motoji, Yasutaka NAKASHIMA, Masahiko HASHIMOTO, and Tsuyoshi YANAGA. "Obstacle-detection bumper for a semi-automatic mower robot suitable for steep slope field." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 2A2—C01. http://dx.doi.org/10.1299/jsmermd.2018.2a2-c01.
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Rashmi, H.N, K.B Ayyappa, H.B Veeresha, K.S Madhu, and K. Basavaraja. "Semi-Auto Grass Cutting & Pesticides Spraying System." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Refereed Multidisciplinary Research Journal 4, no. 1 (2025): 104–8. https://doi.org/10.5281/zenodo.14794833.
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This concept of the solar-powered automatic grass cutter is truly an innovative and sustainable solution for modern lawn care. It provides numerous advantages, including reducing the reliance on fossil fuels, lowering operational costs, and minimizing carbon emissions. With the integration of solar panels and rechargeable batteries, it offers an energy-efficient alternative for maintaining lawns, reducing both the environmental impact and the need for frequent recharging or external power sources.The autonomous navigation and advanced sensors for obstacle detection ensure that the cutter can handle various terrains and obstacles, making it versatile for different types of lawns.
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Perić, Staniša, Marko Milojković, Sergiu-Dan Stan, Milan Banić, and Dragan Antić. "Dealing with Low Quality Images in Railway Obstacle Detection System." Applied Sciences 12, no. 6 (2022): 3041. http://dx.doi.org/10.3390/app12063041.
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Object recognition and classification as well as obstacle distance calculation are of the utmost importance in today’s autonomous driving systems. One such system designed to detect obstacle and track intrusion in railways is considered in this paper. The heart of this system is the decision support system (DSS), which is in charge of making complex decisions, important for a safe and efficient autonomous train drive based on the information obtained from various sensors. DSS determines the object class and its distance from a running train by analyzing sensor images using machine learning algorithms. For the quality training of these machine learning models, it is necessary to provide training sets with images of adequate quality, which is often not the case in real-world railway applications. Furthermore, the images of insufficient quality should not be processed at all in order to save computational time. One of the most common types of distortion which occurs in real-world conditions (train movement and vibrations, movement of other objects, bad weather conditions, and day and night image differences) is blur. This paper presents an improved edge-detection method for the automatic detection and rejection of images of inadequate quality regarding the blur level. The proposed method, with its improvements convenient for railway application, is compared with several other state-of-the-art methods for blur detection, and its superior overall performance is demonstrated.
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Zhang, Yan, and Qi Zhang. "The Visibility of Multisensor Fusion Technology in Public Art Design." Journal of Sensors 2021 (December 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/6426042.
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This exploration is aimed at improving the efficiency and safety of fully automatic line operation in public space. From the perspective of multisensor fusion technology, the definition, development status, and design principles of public art, as well as the related applications of multisensor fusion technology theory and image fusion theory, are introduced by consulting relevant literature. Then, the scheme of subway platform door gap antipinch detection system based on multisensor fusion and the obstacle detection method of tram in transit are proposed. Finally, through the method of questionnaire, the passengers’ cognition of public art in subway space and the concerned components of public art in subway space are analyzed. The results show that the subway space art content loved by passengers is mainly daily life, and the degree of love can reach 28%, followed by local customs, culture, and fashion trends, with a degree of love of 23%. Besides, the problem of obstacle detection of tram in transit is also studied, and a new obstacle detection method combining visual sensor transmission detection and lidar detection is proposed. This method can quickly and accurately identify unsafe factors. Therefore, the research on the visibility of multisensor fusion technology in public art design has great reference significance for the rapid development of transportation industry.
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.P.Margale, Ketan, and S. D. .Mangate. "Smart and automatic wheelchair using Arduino Uno." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44224.
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This paper presents a novel approach to wheelchair control using hand gesture recognition, enabling enhanced mobility for individuals with disabilities. The system employs computer vision and sensor-based technologies to interpret predefined hand gestures, translating them into wheelchair movement commands. Using machine learning algorithms, the model ensures accurate gesture recognition with minimal latency. The proposed system enhances accessibility by reducing dependence on physical joysticks or voice commands, making it suitable for users with varying motor abilities. Experimental results demonstrate high accuracy in gesture detection and smooth wheelchair navigation. Future work includes optimizing real-time processing and integrating adaptive learning for personalized control. Keywords— Arduino Uno, Smart Wheelchair, Autonomous Navigation, Obstacle Avoidance, Controller, MPU-6050 sensor, DC Motors
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Mahmood, Ammar, Mohammed Bennamoun, Senjian An, et al. "Automatic detection of Western rock lobster using synthetic data." ICES Journal of Marine Science 77, no. 4 (2019): 1308–17. http://dx.doi.org/10.1093/icesjms/fsz223.
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Abstract Underwater imaging is being extensively used for monitoring the abundance of lobster species and their biodiversity in their local habitats. However, manual assessment of these images requires a huge amount of human effort. In this article, we propose to automate the process of lobster detection using a deep learning technique. A major obstacle in deploying such an automatic framework for the localization of lobsters in diverse environments is the lack of large annotated training datasets. Generating synthetic datasets to train these object detection models has become a popular approach. However, the current synthetic data generation frameworks rely on automatic segmentation of objects of interest, which becomes difficult when the objects have a complex shape, such as lobster. To overcome this limitation, we propose an approach to synthetically generate parts of the lobster. To handle the variability of real-world images, these parts were inserted into a set of diverse background marine images to generate a large synthetic dataset. A state-of-the-art object detector was trained using this synthetic parts dataset and tested on the challenging task of Western rock lobster detection in West Australian seas. To the best of our knowledge, this is the first automatic lobster detection technique for partially visible and occluded lobsters.
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Liu, Xiaoquan, Xinwei Wang, Pengdao Ren, Yinan Cao, Yan Zhou, and Yuliang Liu. "Automatic fishing net detection and recognition based on optical gated viewing for underwater obstacle avoidance." Optical Engineering 56, no. 08 (2017): 1. http://dx.doi.org/10.1117/1.oe.56.8.083101.
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ISHIHARA, Yuya, Ryo SANO, and Daisuke SEKIMORI. "909 Development of a Walk Assistance Container with Obstacle Detection Function and Automatic Brake Function." Proceedings of Conference of Kansai Branch 2013.88 (2013): _9–9_. http://dx.doi.org/10.1299/jsmekansai.2013.88._9-9_.
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