Small target detection based on faster R-CNN

Mengfan Zhang; Yu Su; Xinping Hu

doi:10.1117/12.2660388

3 February 2023 Small target detection based on faster R-CNN

Mengfan Zhang, Yu Su, Xinping Hu

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 1251113 (2023) https://doi.org/10.1117/12.2660388
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

In order to solve the problem of missing detection due to small targets and to improve the accuracy of small target detection, this paper proposes a small target detection algorithm based on Faster R-CNN. In order to overcome the problem of gradient disappearance and gradient explosion caused by the over-deep network, this paper uses the residual network ResNet50 instead of the VGG16 backbone feature extraction network, and additionally uses a soft non-maximum suppression method to improve the recognition rate of overlapping objects. The algorithm was trained and tested on the PASCAL VOC dataset, and experiments comparing various networks showed that the algorithm showed good detection and high accuracy in the presence of local occlusion of the target as well as in the presence of too small targets, with a detection accuracy of 83.26% on the test set, which was on average 8.45% higher than the traditional Faster R-CNN detection results.

Citation Download Citation

Mengfan Zhang, Yu Su, and Xinping Hu "Small target detection based on faster R-CNN", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 1251113 (3 February 2023); https://doi.org/10.1117/12.2660388

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