支持抗光照目标检测的改进YOLO算法

doi:10.19562/j.chinasae.qcgc.2023.05.007

Abstract

Abstract:

For the problems of unsatisfactory detection accuracy and weak real-time performance in the complicated illumination scenes in the existing deep learning target detection algorithms， an anti-illumination target detection network model YOLO-RLG based on the YOLO algorithm is proposed. Firstly， the RGB data of the input model is converted into HSV data， and the S channel with powerful anti-illumination capability is separated from the HSV data and fused with the RGB data to generate RGBS data so that the input data has anti-illumination capability. Secondly， the backbone network of YOLOV4 is replaced with Ghostnet network， with the model assignment ratio between ordinary convolution and cheap convolution modified to improve the detection efficiency while ensuring the detection accuracy. Finally， the loss function of the model is improved by replacing CIoU with EIoU， which enhances the target detection accuracy and algorithm robustness. The experimental results based on KITTI and VOC datasets indicate that， compared with the original network model， the FPS improves by 22.54 and 17.84 f/s， with the model reduced by 210.3 M， the accuracy （AP） improved by 0.83% and 1.31%， and the algorithm's anti-illumination performance significantly enhanced.

Key words: machine vision, anti-illumination image processing, Ghostnet network, loss function

Yujie Yao,Yuhui Peng,Zehui Chen,Weikun He,Qing Wu,Wei Huang,Wenqiang Chen. An Improved YOLO Algorithm Supporting Anti-illumination Target Detection[J].Automotive Engineering, 2023, 45(5): 777-785.

Figures/Tables 13

References 23

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模型	抗光照增强	Ghostnet替换	Ghostnet改进	EIoU	AP/%	MS/M	FPS/（f·s^-1）
YOLOV4	×	×	×	×	92.53	256.8	34.04
Model 1	√	×	×	×	94.08	263.7	34.22
Model 2	√	√	×	×	88.75	45.4	52.63
Model 3	√	√	√	×	92.62	46.5	55.91
YOLO-RLG	√	√	√	√	93.36	46.5	56.58

模型	AP/%	MS/M	FPS/（f·s^-1）
Faster-RCNN^［18］	77.09	522.4	12.47
SSD300^［19］	81.59	50.6	56.71
SSD512^［20］	84.81	100.2	26.24
SSD-MoblienetV3^［21］	85.6	16.9	55.32
YOLOV4	92.53	256.8	34.04
YOLOV5	90.93	180.5	38.58
EfficientDet^［22］	91.57	355.0	15.50
Swin Transformer^［23］	96.34	1 149.3	29.26
YOLO-RLG	93.36	46.5	56.58

模型	mAP/%	Size/M	FPS/（f·s^-1）
YOLOV4	91.33	256.8	27.04
YOLO-RLG	92.64	46.5	44.88

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An Improved YOLO Algorithm Supporting Anti-illumination Target Detection

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