Surface garbage target detection technology based on deep learning

Shurong Peng; Huixia Chen; Lijuan Guo; Jieni He; Jiayi Peng

doi:10.1117/12.3012202

21 December 2023 Surface garbage target detection technology based on deep learning

Shurong Peng, Huixia Chen, Lijuan Guo, Jieni He, Jiayi Peng

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297002 (2023) https://doi.org/10.1117/12.3012202
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Aiming at the problem that a large amount of water surface garbage causes serious pollution to the environment and is difficult to clean up in time, artificial intelligence equipment and machine vision target detection are combined. A deep learning-based lightweight floating object detection algorithm MobileNetV3-YOLOv4. MobileNetV3-YOLOv4 proposes to use a depth wise separable convolution method to replace the 3 × 3 conventional convolution in the network, replaces the original CSPDarknet53 backbone feature extraction network with MobileNetV3 network, and proposes to use a synthetic fusion pyramid network (SFPN) in the neck network of the model. A synthetic fusion Pyramid Network can reduce the gap between the detection accuracy and the original model while reducing the number of parameters and calculations. The number of parameters and calculations is only 20% of the original model, and the detection speed reaches 96 frames per second. Compared with the original YOLOv4 model, it is 69 frames per second, an increase of 39.1%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shurong Peng, Huixia Chen, Lijuan Guo, Jieni He, and Jiayi Peng "Surface garbage target detection technology based on deep learning", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297002 (21 December 2023); https://doi.org/10.1117/12.3012202

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