Relevant bibliographies by topics / MobileNet-SSD

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'MobileNet-SSD'

Author: Grafiati
Published: 25 July 2021
Last updated: 4 February 2022

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Journal articles on the topic "MobileNet-SSD"

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Li, Yiting, Haisong Huang, Qingsheng Xie, Liguo Yao, and Qipeng Chen. "Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD." Applied Sciences 8, no. 9 (September 17, 2018): 1678. http://dx.doi.org/10.3390/app8091678.

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This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.
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Husein, Amir Mahmud, Christopher Christopher, Andy Gracia, Rio Brandlee, and Muhammad Haris Hasibuan. "Deep Neural Networks Approach for Monitoring Vehicles on the Highway." SinkrOn 4, no. 2 (April 14, 2020): 163. http://dx.doi.org/10.33395/sinkron.v4i2.10553.

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Vehicle classification and detection aims to extract certain types of vehicle information from images or videos containing vehicles and is one of the important things in a smart transportation system. However, due to the different size of the vehicle, it became a challenge that directly and interested many researchers . In this paper, we compare YOLOv3's one-stage detection method with MobileNet-SSD for direct vehicle detection on a highway vehicle video dataset specifically recorded using two cellular devices on highway activities in Medan City, producing 42 videos, both methods evaluated based on Mean Average Precision (mAP) where YOLOv3 produces better accuracy of 81.9% compared to MobileNet-SSD at 67.9%, but the size of the resulting video file detection is greater. Mobilenet-SSD performs faster with smaller video output sizes, but it is difficult to detect small objects.
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Biswas, Debojit, Hongbo Su, Chengyi Wang, Aleksandar Stevanovic, and Weimin Wang. "An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD." Physics and Chemistry of the Earth, Parts A/B/C 110 (April 2019): 176–84. http://dx.doi.org/10.1016/j.pce.2018.12.001.

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Ramalingam, Balakrishnan, Vega-Heredia Manuel, Mohan Rajesh Elara, Ayyalusami Vengadesh, Anirudh Krishna Lakshmanan, Muhammad Ilyas, and Tan Jun Yuan James. "Visual Inspection of the Aircraft Surface Using a Teleoperated Reconfigurable Climbing Robot and Enhanced Deep Learning Technique." International Journal of Aerospace Engineering 2019 (September 12, 2019): 1–14. http://dx.doi.org/10.1155/2019/5137139.

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Aircraft surface inspection includes detecting surface defects caused by corrosion and cracks and stains from the oil spill, grease, dirt sediments, etc. In the conventional aircraft surface inspection process, human visual inspection is performed which is time-consuming and inefficient whereas robots with onboard vision systems can inspect the aircraft skin safely, quickly, and accurately. This work proposes an aircraft surface defect and stain detection model using a reconfigurable climbing robot and an enhanced deep learning algorithm. A reconfigurable, teleoperated robot, named as “Kiropter,” is designed to capture the aircraft surface images with an onboard RGB camera. An enhanced SSD MobileNet framework is proposed for stain and defect detection from these images. A Self-filtering-based periodic pattern detection filter has been included in the SSD MobileNet deep learning framework to achieve the enhanced detection of the stains and defects on the aircraft skin images. The model has been tested with real aircraft surface images acquired from a Boeing 737 and a compact aircraft’s surface using the teleoperated robot. The experimental results prove that the enhanced SSD MobileNet framework achieves improved detection accuracy of aircraft surface defects and stains as compared to the conventional models.
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Nizar, Muhammad Hanif Ahmad, Chow Khuen Chan, Azira Khalil, Ahmad Khairuddin Mohamed Yusof, and Khin Wee Lai. "Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 5 (May 28, 2020): 584–91. http://dx.doi.org/10.2174/1573405615666190114151255.

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Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.
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Barba-Guaman, Luis, José Eugenio Naranjo, and Anthony Ortiz. "Deep Learning Framework for Vehicle and Pedestrian Detection in Rural Roads on an Embedded GPU." Electronics 9, no. 4 (March 31, 2020): 589. http://dx.doi.org/10.3390/electronics9040589.

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Object detection, one of the most fundamental and challenging problems in computer vision. Nowadays some dedicated embedded systems have emerged as a powerful strategy for deliver high processing capabilities including the NVIDIA Jetson family. The aim of the present work is the recognition of objects in complex rural areas through an embedded system, as well as the verification of accuracy and processing time. For this purpose, a low power embedded Graphics Processing Unit (Jetson Nano) has been selected, which allows multiple neural networks to be run in simultaneous and a computer vision algorithm to be applied for image recognition. As well, the performance of these deep learning neural networks such as ssd-mobilenet v1 and v2, pednet, multiped and ssd-inception v2 has been tested. Moreover, it was found that the accuracy and processing time were in some cases improved when all the models suggested in the research were applied. The pednet network model provides a high performance in pedestrian recognition, however, the sdd-mobilenet v2 and ssd-inception v2 models are better at detecting other objects such as vehicles in complex scenarios.
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Evan, Meirista Wulandari, and Eko Syamsudin. "Recognition of Pedestrian Traffic Light using Tensorflow And SSD MobileNet V2." IOP Conference Series: Materials Science and Engineering 1007 (December 31, 2020): 012022. http://dx.doi.org/10.1088/1757-899x/1007/1/012022.

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Finogeev, E., V. Gorbatsevich, A. Moiseenko, Y. Vizilter, and O. Vygolov. "KNOWLEDGE DISTILLATION USING GANS FOR FAST OBJECT DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 583–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-583-2020.

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Abstract. In this paper, we propose a new method for knowledge distilling based on generative adversarial networks. Discriminator CNNs is used as an adaptive knowledge distilling loss. In experiments, single shot multibox detector SSD based on MobileNet v2 and ShuffleNet v1 are used as student networks. Our tests showed AP and mAP improvement of more than 3% on PascalVOC and 1% on MS Coco datasets compared with the baseline algorithm without any architecture or dataset changes. The proposed approach is general and can be used not only with SSD but also with any type of object detection algorithms.
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Kim, Woonki, Fatemeh Dehghan, and Seongwon Cho. "Vehicle License Plate Recognition System using SSD-Mobilenet and ResNet for Mobile Device." Korean Institute of Smart Media 9, no. 2 (June 30, 2020): 92–98. http://dx.doi.org/10.30693/smj.2020.9.2.92.

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Zhang, Jindong, Jiabin Xu, Linyao Zhu, Kunpeng Zhang, Tong Liu, Donghui Wang, and Xue Wang. "An improved MobileNet-SSD algorithm for automatic defect detection on vehicle body paint." Multimedia Tools and Applications 79, no. 31-32 (June 8, 2020): 23367–85. http://dx.doi.org/10.1007/s11042-020-09152-6.

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Dissertations / Theses on the topic "MobileNet-SSD"

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(11173185), Tahrir Ibraq Siddiqui. "Train Solver Protoxt files for Combo 5 and Combo 15." 2021.

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(11173185), Tahrir Ibraq Siddiqui. "Training plots for Combo 5 and 15." 2021.

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(11173185), Tahrir Ibraq Siddiqui. "Training Images." 2021.

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(11173185), Tahrir Ibraq Siddiqui. "Annotations." 2021.

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(11173185), Tahrir Ibraq Siddiqui. "Demos after First Training Run." 2021.

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(11173185), Tahrir Ibraq Siddiqui. "Combo 5 and Combo 15 Demos." 2021.

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(11173185), Tahrir Ibraq Siddiqui. "Intelligent Collision Prevention System For SPECT Detectors by Implementing Deep Learning Based Real-Time Object Detection." Thesis, 2021.

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The SPECT-CT machines manufactured by Siemens consists of two heavy detector heads(~1500lbs each) that are moved into various configurations for radionuclide imaging. These detectors are driven by large torque powered by motors in the gantry that enable linear and rotational motion. If the detectors collide with large objects – stools, tables, patient extremities, etc. – they are very likely to damage the objects and get damaged as well. This research work proposes an intelligent real-time object detection system to prevent collisions between detector heads and external objects in the path of the detector’s motion by implementing an end-to-end deep learning object detector. The research extensively documents all the work done in identifying the most suitable object detection framework for this use case, collecting, and processing the image dataset of target objects, training the deep neural net to detect target objects, deploying the trained deep neural net in live demos by implementing a real-time object detection application written in Python, improving the model’s performance, and finally investigating methods to stop detector motion upon detecting external objects in the collision region. We successfully demonstrated that a Caffe version of MobileNet-SSD can be trained and deployed to detect target objects entering the collision region in real-time by following the methodologies outlined in this paper. We then laid out the future work that must be done in order to bring this system into production, such as training the model to detect all possible objects that may be found in the collision region, controlling the activation of the RTOD application, and efficiently stopping the detector motion.

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Book chapters on the topic "MobileNet-SSD"

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Hung, Phan Duy, and Nguyen Ngoc Kien. "SSD-Mobilenet Implementation for Classifying Fish Species." In Advances in Intelligent Systems and Computing, 399–408. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33585-4_40.

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Xie, Yuxuan, Bing Liu, Lei Feng, Xipeng Li, and Danyin Zou. "A FPGA-Oriented Quantization Scheme for MobileNet-SSD." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 95–103. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9710-3_10.

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Zhou, Jianwen, Wenjing Zhao, Lei Guo, Xinying Xu, and Gang Xie. "Real Time Detection of Surface Defects with Inception-Based MobileNet-SSD Detection Network." In Advances in Brain Inspired Cognitive Systems, 510–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_49.

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Sanjay Kumar, K. K. R., Goutham Subramani, Senthil Kumar Thangavel, and Latha Parameswaran. "A Mobile-Based Framework for Detecting Objects Using SSD-MobileNet in Indoor Environment." In Intelligence in Big Data Technologies—Beyond the Hype, 65–76. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5285-4_6.

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Qian, Yuhan, Guoqiang Wu, Haohui Sun, Wenjuan Li, and Yue Xu. "Research on Small Object Detection in UAV Reconnaissance Images Based on Haar-Like Features and MobileNet-SSD Algorithm." In Advances in Intelligent Systems and Computing, 708–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70042-3_101.

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Levkovits-Scherer, Daniel S., Israel Cruz-Vega, and José Martinez-Carranza. "Real-Time Monocular Vision-Based UAV Obstacle Detection and Collision Avoidance in GPS-Denied Outdoor Environments Using CNN MobileNet-SSD." In Advances in Soft Computing, 613–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33749-0_49.

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Conference papers on the topic "MobileNet-SSD"

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Fernandez, Ibai Gorordo, and Chikamune Wada. "Shoe Detection Using SSD-MobileNet Architecture." In 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 2020. http://dx.doi.org/10.1109/lifetech48969.2020.1570618965.

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Ali, Hashir, Mahrukh Khursheed, Syeda Kulsoom Fatima, Syed Muhammad Shuja, and Shaheena Noor. "Object Recognition for Dental Instruments Using SSD-MobileNet." In 2019 International Conference on Information Science and Communication Technology (ICISCT). IEEE, 2019. http://dx.doi.org/10.1109/cisct.2019.8777441.

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Thokrairak, Sorawit, Kittiya Thibuy, and Prajaks Jitngernmadan. "Valuable Waste Classification Modeling based on SSD-MobileNet." In 2020 5th International Conference on Information Technology (InCIT). IEEE, 2020. http://dx.doi.org/10.1109/incit50588.2020.9310928.

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Jian, Zhang, Zhang Yonghui, Yan Yan, Lin Ruonan, and Wang Xueyao. "MobileNet-SSD with adaptive expansion of receptive field." In 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). IEEE, 2020. http://dx.doi.org/10.1109/iicspi51290.2020.9332204.

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Yu, Guoyan, Lin Wang, Mingxin Hou, Yicha Liang, and Taihua He. "An adaptive dead fish detection approach using SSD-MobileNet." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9326648.

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Zhang, Fei, Qi Li, Yushu Ren, Huixin Xu, Yu Song, and Shuhua Liu. "An Expression Recognition Method on Robots Based on Mobilenet V2-SSD." In 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 2019. http://dx.doi.org/10.1109/icsai48974.2019.9010173.

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Yao, Yufeng, Zixiang Qiu, and Ming Zhong. "Application of improved MobileNet-SSD on underwater sea cucumber detection robot." In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2019. http://dx.doi.org/10.1109/iaeac47372.2019.8997970.

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Sun, Weiqi, Sicong Chen, Liangren Shi, Yuanlong Li, and Zongli Lin. "Vehicle Following in Intelligent Multi-Vehicle Systems Based on SSD-MobileNet." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996181.

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Heredia, Andres, and Gabriel Barros-Gavilanes. "Video processing inside embedded devices using SSD-Mobilenet to count mobility actors." In 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI). IEEE, 2019. http://dx.doi.org/10.1109/colcaci.2019.8781798.

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Younis, Ayesha, Li Shixin, Shelembi Jn, and Zhang Hai. "Real-Time Object Detection Using Pre-Trained Deep Learning Models MobileNet-SSD." In ICCDE 2020: 2020 The 6th International Conference on Computing and Data Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3379247.3379264.

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