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Journal articles on the topic 'Object detection accuracy'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Wang, Zhiyuan, Shujun Men, Yuntian Bai, et al. "Improved Small Object Detection Algorithm CRL-YOLOv5." Sensors 24, no. 19 (2024): 6437. http://dx.doi.org/10.3390/s24196437.

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Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module i

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Zhou, Lei, and Jingke Xu. "Enhanced Abandoned Object Detection through Adaptive Dual-Background Modeling and SAO-YOLO Integration." Sensors 24, no. 20 (2024): 6572. http://dx.doi.org/10.3390/s24206572.

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Abandoned object detection is a critical task in the field of public safety. However, existing methods perform poorly when detecting small and occluded objects, leading to high false detection and missed detection rates. To address this issue, this paper proposes an abandoned object detection method that integrates an adaptive dual-background model with SAO-YOLO (Small Abandoned Object YOLO). The goal is to reduce false and missed detection rates for small and occluded objects, thereby improving overall detection accuracy. First, the paper introduces an adaptive dual-background model that adju

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Liu, Zhiguo, Enzheng Zhang, Qian Ding, Weijie Liao, and Zixiang Wu. "An Improved Method for Enhancing the Accuracy and Speed of Dynamic Object Detection Based on YOLOv8s." Sensors 25, no. 1 (2024): 85. https://doi.org/10.3390/s25010085.

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Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the

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Dawn, Wilson, Manusankar C. Dr., and Prathibha P. H. Dr. "Analytical Study on Object Detection using Yolo Algorithm." International Journal of Innovative Science and Research Technology 7, no. 8 (2022): 587–89. https://doi.org/10.5281/zenodo.7036535.

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Object detection is a technique that allows detecting and locating objects in videos and images. Object detection is widely used to count objects in a scene, track their precise locations and accurately label the objects. It seeks to answer what is the object? and Where is it? . Object detection adopts various approaches such as fast R-CNN, Retina-Net, Single Shot MultiBox Detector (SSD) and YOLO. Among these, YOLO is the most powerful algorithm for object detection and as well as suited for real-time scenarios. It is popular because of its accuracy and speed. YOLO uses Neural networks to prov

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S. Hasan, Athraa, Jianjun Yi, Haider M. AlSabbagh, and Liwei Chen. "Multiple Object Detection-Based Machine Learning Techniques." Iraqi Journal for Electrical and Electronic Engineering 20, no. 1 (2024): 149–59. http://dx.doi.org/10.37917/ijeee.20.1.15.

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Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic

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Ogunrinde, Isaac, and Shonda Bernadin. "Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions." Sensors 23, no. 14 (2023): 6255. http://dx.doi.org/10.3390/s23146255.

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AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained

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K, Chidananda, Maloth Gulshan Naik, Yama Mohan, Neerati Madhavan, Sheik Afzal Arfan, and Ashish Kativarapu. "An efficient novel paradigm for object detection through web camera using deep learning (YOLOv5’s object detection model)." E3S Web of Conferences 391 (2023): 01093. http://dx.doi.org/10.1051/e3sconf/202339101093.

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Object detection, a fundamental duty in computer vision that has a wide range of practical applications, they are surveillance, robotics, and autonomous driving. Recent developments of deep learning have got gradual improvemenrts in detection accuracy and speed. One of the most popular and effective deep learning models for object detection is YOLOv5. In this discussion, we an object detection model through YOLOv5 and its implementation for object detection tasks. We discuss the model’s architecture, training process, and evaluation metrics. Furthermore, we present experimental results on popu

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Meng, Jintao, Shaokai Shen, Jiaqi Wang, and Chunjian Zhou. "Object Detection Algorithms Based on Deep Learning: A Review." Asian Journal of Research in Computer Science 17, no. 7 (2024): 145–56. http://dx.doi.org/10.9734/ajrcos/2024/v17i7485.

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With the continuous development of deep learning, object detection algorithms based on deep learning have made significant progress in the field of computer vision, widely applied in areas such as autonomous driving, industrial inspection, agriculture, transportation, and medicine. Traditional object detection algorithms face issues such as low detection efficiency and poor robustness. However, deep learning-based object detection algorithms significantly enhance detection accuracy and generalization by learning low-level and high-level image features. This article first introduces traditional

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Xu, Xin, Bin Liu, Yun Zhong, and Song Hua. "Lightweight Detection Algorithm for Air-to-Ground Threat Object." Journal of Physics: Conference Series 2872, no. 1 (2024): 012032. http://dx.doi.org/10.1088/1742-6596/2872/1/012032.

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Abstract In air-to-ground threat object detection, the typically strong concealment, small size, and high movement speed of threat objects often lead to challenges such as missed detections, low accuracy, and an excessive number of network parameters. An efficient lightweight detection algorithm for air-to-ground threat objects is proposed, built upon the YOLOv7-tiny framework. To address the issue of large network parameters, lightweight modules ELAN-GS and Slim-Neck are employed. Additionally, to refine the detection precision for small objects, the NWD metric and NWD loss function are intro

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Ting-Na Liu, Ting-Na Liu, Zhong-Jie Zhu Ting-Na Liu, Yong-Qiang Bai Zhong-Jie Zhu, Guang-Long Liao Yong-Qiang Bai, and Yin-Xue Chen Guang-Long Liao. "YOLO-Based Efficient Vehicle Object Detection." 電腦學刊 33, no. 4 (2022): 069–79. http://dx.doi.org/10.53106/199115992022083304006.

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<p>Vehicle detection is one of the key techniques of intelligent transportation system with high requirements for accuracy and real-time. However, the existing algorithms suffer from the contradiction between detec-tion speed and detection accuracy, and weak generalization ability. To address these issues, an improved vehicle detection algorithm is presented based on the You Only Look Once (YOLO). On the one hand, an efficient feature extraction network is restructured to speed up the feature transfer of the object, and re-use the feature information extracted from the input image. On th

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Amany, Yehia, and A. Taie Shereen. "RGB-D and corrupted images in assistive blind systems in smart cities." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 1970~1982. https://doi.org/10.11591/eei.v11i4.3770.

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Assistive blind systems or assistive systems for visually impaired in smart cities help visually impaired to perform their daily tasks faced two problems when using you only look once version 3 (YOLOv3) object detection. Object recognition is a significant technique used to recognize objects with different technologies, algorithms, and structures. Object detection is a computer vision technique that identifies and locates instances of objects in images or videos. YOLOv3 is the most recent object detection technique that introduces promising results. YOLOv3 object detection task is to determine

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Zhu, Chunhua, Jiarui Liang, and Fei Zhou. "Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection." Information 14, no. 10 (2023): 560. http://dx.doi.org/10.3390/info14100560.

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Stemming from the overlap of objects and undertraining due to few samples, road dense object detection is confronted with poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects on the road has been proposed. Firstly, the Darknet-53 network structure is adopted to obtain a pre-trained YOLOv3 model. Then, the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles. In the proposed model, one random function is adapted to init

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Madhan, K., and N. Shanmugapriya. "Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos." Indian Journal of Information Sources and Services 14, no. 1 (2024): 9–16. http://dx.doi.org/10.51983/ijiss-2024.14.1.3895.

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Object detection and classification have become prominent research topics in computer vision due to their applications in areas such as visual tracking. Despite advancements, vision-based methods for detecting smaller targets and densely packed objects with high accuracy in complex dynamic environments still encounter challenges. This paper introduces a novel and enhanced approach for hyperbolic shadow detection and object classification based on the Enhanced Moving Object Detection (EMOD) algorithm and an improved manta ray-based convolutional neural network optimized for search. In the prepr

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Subagja, Mifta, and Ben Rahman. "Object Detection Using YOLOv5 and OpenCV." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 13, no. 1 (2025): 155–62. https://doi.org/10.33558/piksel.v13i1.10772.

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Object detection is one of the main tasks in computer vision, aimed at recognizing and localizing objects in images or videos. In this study, we utilize the YOLOv5 model, which is well known for its efficiency in realtime object detection. We implement this method with the help of the OpenCV library for image processing. This research aims to evaluate the performance of YOLOv5 in detecting objects in various types of images, including landscape photos, cat photos, and traffic light images with vehicles. The model is trained using optimization methods with the Adam optimizer and assessed throug

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Xi, Xiaoli, Jinxin Wang, Fang Li, and Dongmei Li. "IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps." Electronics 11, no. 14 (2022): 2154. http://dx.doi.org/10.3390/electronics11142154.

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Detecting small objects in infrared images remains a challenge because most of them lack shape and texture. In this study, we proposed an infrared small-object detection method to improve the capacity for detecting thermal objects in complex scenarios. First, a sparse-skip connection block is proposed to enhance the response of small infrared objects and suppress the background response. This block is used to construct the detection model backbone. Second, a region attention module is designed to emphasize the features of infrared small objects and suppress background regions. Finally, a batch

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Lakhotiya, Rushikesh, Mayuresh Chavan, Satwik Divate, and Soham Pande. "Image Detection and Real Time Object Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2785–90. http://dx.doi.org/10.22214/ijraset.2023.51839.

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Abstract: Object detection is a crucial task in computer vision with various practical applications, including surveillance, autonomous vehicles, and robotics. The YOLO (You Only Look Once) algorithm is a popular real-time object detection algorithm that has gained significant attention due to its high accuracy and speed. This algorithm processes the entire image at once and predicts bounding boxes and class probabilities for identified objects, making it ideal for time-sensitive applications. YOLO has evolved through various versions, with YOLOv5 being the latest and most advanced version tha

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Yao, Bin, Chengkun Zhang, Qingxiang Meng, et al. "SRM-YOLO for Small Object Detection in Remote Sensing Images." Remote Sensing 17, no. 12 (2025): 2099. https://doi.org/10.3390/rs17122099.

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Small object detection presents significant challenges in computer vision, often affected by factors such as low resolution, dense object distribution, and complex backgrounds, which can lead to false positives or missed detections. In this paper, we introduce SRM-YOLO, a novel small object detection algorithm based on the YOLOv8 framework. The model incorporates the following key innovations: Reuse Fusion Structure (RFS), which enhances feature fusion; SPD-Conv, which enables effective downsampling while preserving critical information; and a specialized detection head designed for small obje

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Juewen Hu, Juewen Hu, Pei Wang Juewen Hu, Jian Yang Pei Wang, and Longqiang Ni Jian Yang. "An Improved Algorithm for Moving Object Detection in YOLO UAV Videos." 電腦學刊 33, no. 3 (2022): 147–58. http://dx.doi.org/10.53106/199115992022063303012.

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<p>Recently, moving object detection (MOD) in UAV (Unmanned Aerial. Vehicle) videos has been widely used in many fields. However, different objects and different algorithms often result in different detection accuracy. SSD (Single Shot MultiBox Detector) series and YOLO (You Only Look Once) version 5 are two popular object detection model, and their performance are always evaluated and compared with other improved method for optimizing detection accuracy. In this paper, an improved YOLO_v5 detection algorithm was proposed to further improve the detection accuracy. It adopted a cascaded i

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Dhivya S, Dhanusya P, PriyaDharshini N, and Mr. D. Vijayakumar B.E, M. S. "AI Based Real Time Object Detection and Analysis of Multiple Object Existence." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 02 (2025): 240–47. https://doi.org/10.47392/irjaeh.2025.0034.

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Accurate and efficient object detection and counting are crucial in modern computer vision, especially for real-time applications. However, existing methods often struggle with challenges like processing delays, low accuracy in complex environments, and difficulty in detecting overlapping objects. To address these drawbacks, this project proposes an AI-based solution using the YOLO (You Only Look Once) framework, integrated with OpenCV for enhanced image processing. Our system processes video streams as input, performing real-time object detection and counting with high accuracy. It ensures re

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Jia, Yin, Balakrishnan Ramalingam, Rajesh Elara Mohan, Zhenyuan Yang, Zimou Zeng, and Prabakaran Veerajagadheswar. "Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation." Sensors 23, no. 4 (2023): 2337. http://dx.doi.org/10.3390/s23042337.

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Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robo

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M., Chinnarao R. Goutham Sai Kalyan T. Naga Pravallika B. Srinivas. "Object Detection Using Yolo And Tensor Flow." International Journal in Engineering Sciences 1, no. 1 (2024): 13–23. https://doi.org/10.5281/zenodo.11825059.

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Object detection methods aim to identify all target objects in the target image and determine the categories and position information in order to achieve machine vision understanding. Numerous approaches have been proposed to solve this problem, mainly inspired by methods of computer vision and deep learning. However, existing approaches always perform poorly for the detection of small, dense objects, and even fail to detect objects with random geometric transformations. In this study, we compare and analyse mainstream object detection algorithms and propose a multi-scaled deformable convoluti

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Lou, L., S. Zhang, and S. Zhang. "OBJECT DETECTION WITH THE HIGH-FREQUENCY CHANGE OF OBJECTS CLASSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 125–30. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-125-2020.

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Abstract. With the development of deep learning, object detection has a significantly improvement. But most of algorithms only focus on the detection accuracy and speed, they do not consider the difficulty of making training datasets and the time consumption of training detection models, which will have a bad influence on the performance of detection model when the class of objects change in high frequency. This paper proposes a method named double network detection (DN detection), it can improve the efficiency of making training datasets and shorten the time of training model. At the same tim

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Sharma, Aman Kumar. "Object Detection Model: An Enhanced SSD." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1598–612. http://dx.doi.org/10.22214/ijraset.2024.61802.

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Abstract: Object Detection plays a significant role in today’s era. Object detection plays a crucial role in various applications such as surveillance, medical images, and autonomous vehicles. The primary goal of object detection is to accurately detect the boundaries of objects of interest and classify those objects into predefined categories. In this research paper, we enhanced an object detection technique i.e. Single shot multibox detector (SSD) which is one of the top object detection technique in both aspect accuracy and speed. SSD is an object detection model that predicts object boundi

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Zhao, Zuopeng, Zhongxin Zhang, Xinzheng Xu, Yi Xu, Hualin Yan, and Lan Zhang. "A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices." Computational Intelligence and Neuroscience 2020 (December 15, 2020): 1–12. http://dx.doi.org/10.1155/2020/6616584.

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It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices suc

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L, Dr Priya, Poornimathi K, and Dr P. Kumar. "Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation." International Journal of Engineering and Advanced Technology 12, no. 6 (2023): 7–13. http://dx.doi.org/10.35940/ijeat.f4259.0812623.

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Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtai

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Dr., Priya.L, Poornimathi.K, and P. Kumar Dr. "Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 6 (2023): 7–13. https://doi.org/10.35940/ijeat.F4259.0812623.

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<strong>Abstract: </strong>Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching

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Parthasarathy, Mr Logaiyan, SRIRAM S, and HARI HARAN S. "IMMEDIATE OBJECT SPOTTING." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–7. https://doi.org/10.55041/isjem02145.

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Object Spotting has become one of the most essential and challenging tasks in computer vision, particularly in the context of deep learning advancements. This paper reviews the evolution of object detection techniques, highlighting how deep neural networks have transformed the field. We classify the state-of-the-art detection algorithms into three main categories: anchor-based, anchor-free, and transformer-based detectors, each with distinct methodologies for identifying objects in images. This survey includes a comparison of key convolutional neural network (CNN) architectures used for object

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Xie, Ming, Xiaobing Yang, Boxu Li, and Yingjie Fan. "A YOLO-Based Method for Head Detection in Complex Scenes." Sensors 24, no. 22 (2024): 7367. http://dx.doi.org/10.3390/s24227367.

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Detecting objects in intricate scenes has always presented a significant challenge in the field of machine vision. Complex scenes typically refer to situations in images or videos where there are numerous densely distributed and mutually occluded objects, making the object detection task even more difficult. This paper introduces a novel head detection algorithm, YOLO-Based Head Detection in Complex Scenes (YOLO-HDCS). Firstly, in complex scenes, head detection typically involves a large number of small objects that are randomly distributed. Traditional object detection algorithms struggle to

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Lyu, Shijie. "UAV Image Object Detection Based on Attention Mechanism and Dilated Convolution." Applied and Computational Engineering 173, no. 1 (2025): 15–21. https://doi.org/10.54254/2755-2721/2025.24679.

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Existing algorithms for unmanned aerial vehicle (UAV) image object detection often face challenges such as low detection accuracy for small objects and missed detections of multi-scale objects. To address these issues, this paper proposes a UAV image object detection algorithm that integrates a channel attention mechanism with parallel-structured dilated convolution feature fusion. To enhance the algorithms feature representation capabilities in terms of channel attention and receptive field, the ResNet50 backbone is redesigned by incorporating the Squeeze-and-Excitation Network (SENet) and a

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Kang, Hyeong-Ju. "SSDLiteX: Enhancing SSDLite for Small Object Detection." Applied Sciences 13, no. 21 (2023): 12001. http://dx.doi.org/10.3390/app132112001.

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Object detection in many real applications requires the capability of detecting small objects in a system with limited resources. Convolutional neural networks (CNNs) show high performance in object detection, but they are not adequate to resource-limited environments. The combination of MobileNet V2 and SSDLite is one of the common choices in such environments, but it has a problem in detecting small objects. This paper analyzes the structure of SSDLite and proposes variations leading to small object detection improvement. The feature maps with the higher resolution are utilized more, and the

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Chen, Xun, Linyi Deng, Chao Hu, Tianyi Xie, and Chengqi Wang. "Dense Small Object Detection Based on an Improved YOLOv7 Model." Applied Sciences 14, no. 17 (2024): 7665. http://dx.doi.org/10.3390/app14177665.

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Detecting small and densely packed objects in images remains a significant challenge in computer vision. Existing object detection methods often exhibit low accuracy and frequently miss detection when identifying dense small objects and require larger model parameters. This study introduces a novel detection framework designed to address these limitations by integrating advanced feature fusion and optimization techniques. Our approach focuses on enhancing both detection accuracy and parameter efficiency. The approach was evaluated on the open-source VisDrone2019 data set and compared with main

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Wang, Jie, Hong Zhao, Yuxi Huang, and Baotong Zhang. "Improved YOLOv8 Algorithm for Indoor Object Detection." Journal of Physics: Conference Series 3024, no. 1 (2025): 012037. https://doi.org/10.1088/1742-6596/3024/1/012037.

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Abstract To address the challenges of missed detections and false positives in indoor object detection tasks caused by target clustering and occlusion, this study proposes an advanced indoor object detection algorithm, SRSE-YOLOv8. A novel indoor object detection algorithm, SRSE-YOLOv8, is proposed to address missed and false detections caused by object clustering and occlusion in indoor scenes. First, an additional smaller detection head is introduced to enhance the capture of small objects and improve feature extraction capabilities. Then, RFAConv is incorporated to achieve input-adaptive ke

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Lu, Kewei, Fengkui Zhao, Xiaomei Xu, and Yong Zhang. "An object detection algorithm combining self-attention and YOLOv4 in traffic scene." PLOS ONE 18, no. 5 (2023): e0285654. http://dx.doi.org/10.1371/journal.pone.0285654.

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Automobile intelligence is the trend for modern automobiles, of which environment perception is the key technology of intelligent automobile research. For autonomous vehicles, the detection of object information, such as vehicles and pedestrians in traffic scenes is crucial to improving driving safety. However, in the actual traffic scene, there are many special conditions such as object occlusion, small objects, and bad weather, which will affect the accuracy of object detection. In this research, the SwinT-YOLOv4 algorithm is proposed for detecting objects in traffic scenes, which is based o

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Lika, Sudiharyanto, Yonky Pernando, and Ade Kurniawan. "Lightweight Deep Learning for Object Detection on Mobile Device." Bulletin of Informatics and Data Science 2, no. 2 (2023): 106. http://dx.doi.org/10.61944/bids.v2i2.82.

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Computer vision is a research in the development of technology to obtain information from images and replicate or imitate human visual processes, so that computers can know the objects around them. Deep learning is now the key word as a new era in machine learning that trains computers in finding patterns from large amounts of data. The Convolution Neural Networks (CNN) algorithm has proven impressive in terms of performance for detecting objects, image classification and semantic segmentation. Object detection is a technique used to identify the type of object in an image and also the exact l

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Xu, Wenyu, Chaofan Zhang, Qi Wang, and Pangda Dai. "FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection." Sensors 22, no. 18 (2022): 6993. http://dx.doi.org/10.3390/s22186993.

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UAV-based object detection has recently attracted a lot of attention due to its diverse applications. Most of the existing convolution neural network based object detection models can perform well in common object detection cases. However, due to the fact that objects in UAV images are spatially distributed in a very dense manner, these methods have limited performance for UAV-based object detection. In this paper, we propose a novel transformer-based object detection model to improve the accuracy of object detection in UAV images. To detect dense objects competently, an advanced foreground en

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Wang, Jie, and Hong Zhao. "Improved YOLOv8 Algorithm for Water Surface Object Detection." Sensors 24, no. 15 (2024): 5059. http://dx.doi.org/10.3390/s24155059.

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To address the issues of decreased detection accuracy, false detections, and missed detections caused by scale differences between near and distant targets and environmental factors (such as lighting and water waves) in surface target detection tasks for uncrewed vessels, the YOLOv8-MSS algorithm is proposed to be used to optimize the detection of water surface targets. By adding a small target detection head, the model becomes more sensitive and accurate in recognizing small targets. To reduce noise interference caused by complex water surface environments during the downsampling process in t

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Kurniati, Florentina Tatrin, Hindriyanto Dwi Purnomo, Irwan Sembiring, and Ade Iriani. "Digital Image Object Detection with GLCM Multi-Degrees and Ensemble Learning." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 2 (2024): 321–27. http://dx.doi.org/10.29207/resti.v8i2.5597.

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Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level Co-occurrence Matrix Multi-Degrees) extraction method with classification using K-Nearest Neighbours (K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting o

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Zhou, Huaping, Wei Guo, and Qi Zhao. "An Anchor-Free Network for Increasing Attention to Small Objects in High Resolution Remote Sensing Images." Applied Sciences 13, no. 4 (2023): 2073. http://dx.doi.org/10.3390/app13042073.

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Aimed at the problems of small object detection in high resolution remote sensing images, such as difficult detection, diverse scales, and dense distribution, this study proposes a new method, DCE_YOLOX, which is more focused on small objects. The method uses depthwise separable deconvolution for upsampling, which can effectively recover lost feature information and combines dilated convolution and CoTNet to extract local contextual features, which can make full use of the hidden semantic information. At the same time, EcaNet is added to the enhanced feature extraction network of the baseline

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Yehia, Amany, and Shereen A. Taie. "RGB-D and corrupted images in assistive blind systems in smart cities." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 1970–82. http://dx.doi.org/10.11591/eei.v11i4.3770.

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Assistive blind systems or assistive systems for visually impaired in smart cities help visually impaired to perform their daily tasks faced two problems when using you only look once version 3 (YOLOv3) object detection. Object recognition is a significant technique used to recognize objects with different technologies, algorithms, and structures. Object detection is a computer vision technique that identifies and locates instances of objects in images or videos. YOLOv3 is the most recent object detection technique that introduces promising results. YOLOv3 object detection task is to determine

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Skocypec, Rachel M., and Mary A. Peterson. "Semantic Expectation Effects on Object Detection: Using Figure Assignment to Elucidate Mechanisms." Vision 6, no. 1 (2022): 19. http://dx.doi.org/10.3390/vision6010019.

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Recent evidence suggesting that object detection is improved following valid rather than invalid labels implies that semantics influence object detection. It is not clear, however, whether the results index object detection or feature detection. Further, because control conditions were absent and labels and objects were repeated multiple times, the mechanisms are unknown. We assessed object detection via figure assignment, whereby objects are segmented from backgrounds. Masked bipartite displays depicting a portion of a mono-oriented object (a familiar configuration) on one side of a central b

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Han, Tianxin, Lina Sun, and Qing Dong. "An Improved YOLO Model for Traffic Signs Small Target Image Detection." Applied Sciences 13, no. 15 (2023): 8754. http://dx.doi.org/10.3390/app13158754.

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Traffic sign detection significantly reduces traffic accidents, but in real-world scenarios, the considerable distance between traffic signs and in-vehicle cameras means only a small proportion of traffic signs are captured in the images. This makes far-off traffic sign detection a small object detection problem, and with fewer details in small sign objects, detection becomes increasingly challenging. In this paper, we specifically address poor localization, low accuracy, and missed detections when using You Only Look Once Version 5 (YOLOv5) for detecting small traffic sign objects. Firstly, w

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Rizwana Kallooravi Thandil and Umira Parvi KP. "Enhanced object detection in videos using bio vision." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1720–27. http://dx.doi.org/10.30574/wjarr.2024.24.1.3121.

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Video surveillance systems enhance security and monitor various environments. They act as a deterrent to potential threats and provide vital surveillance to maintain safety. A key component of these systems is object detection, which identifies objects of interest and generates critical data for tracking and analysis. However, detecting objects in low-light conditions is particularly challenging due to reduced visibility, low contrast, and shadows, which can obscure objects or cause them to blend into the background. To address these challenges, this research introduces an enhanced object dete

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Rizwana, Kallooravi Thandil, and Parvi KP Umira. "Enhanced object detection in videos using bio vision." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1720–27. https://doi.org/10.5281/zenodo.15039435.

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Video surveillance systems enhance security and monitor various environments. They act as a deterrent to potential threats and provide vital surveillance to maintain safety. A key component of these systems is object detection, which identifies objects of interest and generates critical data for tracking and analysis. However, detecting objects in low-light conditions is particularly challenging due to reduced visibility, low contrast, and shadows, which can obscure objects or cause them to blend into the background. To address these challenges, this research introduces an enhanced object dete

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Muhammad Agus Syaputra, Josua Pinem, Afiq Alghazali Lubis, and Yuva Denia. "Implementasi Algoritma YOLO Dalam Pengklasifikasian Objek Transportasi pada Lalu Lintas Kota Medan." Populer: Jurnal Penelitian Mahasiswa 3, no. 1 (2023): 13–23. http://dx.doi.org/10.58192/populer.v3i1.1641.

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This research allows an automated system for detecting classified means of transportation in Medan City traffic using the YOLOv8 algorithm. The YOLOv8 algorithm is used to detect transportation objects with accuracy that is many times better than other object detection algorithms and with good accuracy after training with various data sets. The use of this algorithm provides an effective solution for handling congestion in the form of increasing the number of vehicles and less orderly traffic users in the city of Medan. The placement of each transportation object in the image to be tested by t

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Juyal, Amit. "A Deep Learning-Based Approach for Real-Time Object Detection and Recognition." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1304–14. http://dx.doi.org/10.17762/msea.v70i2.2322.

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Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, makin

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Jia, Nan, Zongkang Wei, and Bangyu Li. "Attention-Enhanced Lightweight One-Stage Detection Algorithm for Small Objects." Electronics 12, no. 7 (2023): 1607. http://dx.doi.org/10.3390/electronics12071607.

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The majority of object detection algorithms based on convolutional neural network are focused on larger objects. In order to improve the accuracy and efficiency of small object detection, a novel lightweight object detection algorithm with attention enhancement is proposed in this paper. The network part of the proposed algorithm is based on a single-stage framework and takes MobileNetV3-Large as a backbone. The representation of shallower scale features in the scale fusion module is enhanced by introducing an additional injection path from the backbone and a detection head specially responsib

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Luo, Xiaoyue, Yanhui Wang, Benhe Cai, and Zhanxing Li. "Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 742. http://dx.doi.org/10.3390/ijgi10110742.

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Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving o

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Xu, Shufang, Hanqing Tang, Jianni Li, Longbao Wang, Xuejie Zhang, and Hongmin Gao. "A YOLOW Algorithm of Water-Crossing Object Detection." Applied Sciences 13, no. 15 (2023): 8890. http://dx.doi.org/10.3390/app13158890.

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Accurately identifying and locating water-crossing objects is of utmost importance for environmental protection. However, traditional detection algorithms often exhibit poor anti-interference performance and low detection accuracy in complex environments. To address these issues, this paper proposes a YOLOW algorithm based on a one-stage object detection algorithm for the automatic identification of water objects. The proposed algorithm incorporates two new modules, namely the SPDCS module and the SPPAUG module, to improve the model’s performance. Specifically, the SPDCS module retains all inf

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Yang, Dongfang, Xing Liu, Hao He, and Yongfei Li. "Air-to-ground multimodal object detection algorithm based on feature association learning." International Journal of Advanced Robotic Systems 16, no. 3 (2019): 172988141984299. http://dx.doi.org/10.1177/1729881419842995.

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Detecting objects on unmanned aerial vehicles is a hard task, due to the long visual distance and the subsequent small size and lack of view. Besides, the traditional ground observation manners based on visible light camera are sensitive to brightness. This article aims to improve the target detection accuracy in various weather conditions, by using both visible light camera and infrared camera simultaneously. In this article, an association network of multimodal feature maps on the same scene is used to design an object detection algorithm, which is the so-called feature association learning

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Gunawan, Chichi Rizka, Nurdin Nurdin, and Fajriana Fajriana. "Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once)." International Journal of Engineering, Science and Information Technology 2, no. 3 (2022): 96–99. http://dx.doi.org/10.52088/ijesty.v2i3.309.

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Object detection is an activity that aims to gain an understanding of the classification, concept estimation, and location of objects in an image. As one of the fundamental computer vision problems, object detection can provide valuable information for the semantic understanding of images and videos and is associated with many applications, including image classification. Object detection has recently become one of the most exciting fields in computer vision. Detection of objects on this system using YOLOv3. The You Only Look Once (YOLO) method is one of the fastest and most accurate methods f

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