Embedded low illumination enhanced network for object detection

Ziwei Sun; Xiaojun Bi; Sa Yang; Congcong Zhao

doi:10.1117/12.2636934

29 April 2022 Embedded low illumination enhanced network for object detection

Ziwei Sun, Xiaojun Bi, Sa Yang, Congcong Zhao

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 1224710 (2022) https://doi.org/10.1117/12.2636934
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

Object detection under low lighting is still a serious challenge because feature details of a target in an image with low illumination are so indistinct that it is very difficult to extract sufficient features for target detection. Therefore, many lowilluminance enhancement networks are proposed for feature extraction. However, most of the current low-illuminance enhancement methods employ a single network to enhance the input images with different illuminances, resulting in the problem of excessive enhancement for some input images. And it is inevitable to produce excessive noise during the image enhancement. To address such two issues, we propose an embedded low illumination enhanced network based on the Scale-YOLOv4 object detection network, iteratively generating enhancement images with different illumination levels, and among the enhanced images, automatically selecting the one which is best suitable for target detection, and adding a noise removal module to eliminate the generated noise. The proposed embedded low illumination enhanced network is tested on our (ExDark + MS COCO unlabeled) data set, and the effect of 55.7% mAP (70.4% AP50) is achieved, which demonstrates the effectiveness of the proposed network.

Citation Download Citation

Ziwei Sun, Xiaojun Bi, Sa Yang, and Congcong Zhao "Embedded low illumination enhanced network for object detection", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 1224710 (29 April 2022); https://doi.org/10.1117/12.2636934

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KEYWORDS

Image enhancement

Target detection

Denoising

Feature extraction

Convolution

Data modeling

Image processing

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