Abstract
To solve the problems of complex model structure, large number of parameters, and high resource consumption that make it difficult to meet the real-time requirements of embedded target detection tasks, this paper proposed a lightweight target detection algorithm based on improved MobileNetv3-YOLOv3. This algorithm uses MobileNetv3 network to replace the backbone of the original YOLOv3 network, and the reduction of network parameters greatly improves the detection speed of the algorithm; the loss function is modified to CIoU to improve the accuracy and detection speed of the network. The experimental results showed that the improved lightweight detection algorithm on the VOC07 + 12 dataset has a 1.55% improvement in mAP and a 2.47 times improvement in FPS on CPU compared to the original YOLOv3 algorithm. This improved algorithm ensures the detection accuracy based on a significant increase in detection speed, which reflects the theoretical and application value of the research.
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Acknowledgment
This work is supported in part by the National Natural Science Foundation of China under Grant 51475251, the Natural Science Foundation of Shandong Province under Grant ZR2013FM014 and in part by the Qingdao Municipality Livelihood Plan Project under Grant 22-3-7-xdny-18-nsh.
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Fang, T., Du, B., Xue, Y., Yang, G., Zhao, J. (2022). A Lightweight Target Detection Algorithm Based on Improved MobileNetv3-YOLOv3. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_35
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DOI: https://doi.org/10.1007/978-3-031-10989-8_35
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