Downhole target detection based on channel pruning and generalized intersection ratio

Dianxing Chen; Li Wang; Mingqin Wang; Zhengjun Liu; Da Bu; Nasen Li

doi:10.1117/12.2611375

22 October 2021 Downhole target detection based on channel pruning and generalized intersection ratio

Dianxing Chen, Li Wang, Mingqin Wang, Zhengjun Liu, Da Bu, Nasen Li

Proceedings Volume 11928, International Conference on Image Processing and Intelligent Control (IPIC 2021); 1192814 (2021) https://doi.org/10.1117/12.2611375
Event: International Conference on Image Processing and Intelligent Control (IPIC 2021), 2021, Lanzhou, China

Abstract

This paper proposes a method based on channel pruning and generalized intersection ratio. This method focuses on solving the problems that the deployment of the large volume model is difficult and the original loss function cannot reflect the real overlap between the real frame and the predicted frame. The proposed target detection method was compared with the YOLOV3 network model, the R-CNN method and the Faster-CNN method in the VOC2007 dataset. The results show that the proposed target detection method has smaller model size, faster inference speed, higher accuracy and less dependence on computer hardware, which is more suitable for deployment in special environment such as underground coal mine.

Citation Download Citation

Dianxing Chen, Li Wang, Mingqin Wang, Zhengjun Liu, Da Bu, and Nasen Li "Downhole target detection based on channel pruning and generalized intersection ratio", Proc. SPIE 11928, International Conference on Image Processing and Intelligent Control (IPIC 2021), 1192814 (22 October 2021); https://doi.org/10.1117/12.2611375

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