Object detection in infrared images using modified YOLOv4 models and an image enhancement module

Dan Wang; Huiqian Du; Zhifeng Ma

doi:10.1117/12.2680173

27 June 2023 Object detection in infrared images using modified YOLOv4 models and an image enhancement module

Dan Wang, Huiqian Du, Zhifeng Ma

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270521 (2023) https://doi.org/10.1117/12.2680173
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Deep learning-based object detection approaches have shown excellent performance in RGB images. However, when used to detect objects from infrared images, the accuracy may reduce significantly due to low contrast, obscure textures and strong noise of infrared images. To alleviate the problem, we design a detail enhancement module involving spatial attention mechanism to enhance the textures and details of images. The output of the proposed module is fed into modified YOLOv4. We introduce Alpha-IoU loss and Weighted-NMS to YOLOv4 to enhance geometric factors in both bounding box regression and Non-Maximum Suppression, leading to notable gains of average precision. The experiment results show that compared with YOLOv4, mAP0.5 and mAP0.5:0.95 of our model are improved by 1.1% and 3.5% respectively, effectively improving the detection accuracy.

Citation Download Citation

Dan Wang, Huiqian Du, and Zhifeng Ma "Object detection in infrared images using modified YOLOv4 models and an image enhancement module", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270521 (27 June 2023); https://doi.org/10.1117/12.2680173

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