融合图像显著性特征的轻量级目标检测算法

doi:10.19562/j.chinasae.qcgc.2024.01.009

Abstract

Abstract:

For the problems of dense targets， severe edge occlusion， and blurred foreground and background that intelligent vehicles face in actual traffic environments， a lightweight object detection algorithm based on image saliency feature fusion is proposed in this paper. Firstly， salient feature maps are extracted based on grayscale images， and input into convolutional neural networks with color images. Secondly， a lightweight fusion network is constructed using the Ghost Model， and the EIoU is used to optimize the model's border localization loss. In order to enhance the detection accuracy of similar occluded targets， non-maximum suppression algorithm is improved on the backend of the network. Finally， the KITTI dataset is used for training and testing. The experiment shows that the improved detection mAP value of the network reaches 92.7%， with an average accuracy improvement of 3.8% compared to the original network YOLOv5. The accuracy and recall rates are increased by 3% and 6.2%.

Key words: target detection, muti-feature fusion, lightweight network, YOLOv5

Lei Ma, Shunqing Yang, Huanhuan Wang, Jiachen Zhai, Jianao Xu. Lightweight Object Detection Algorithm Based on Image Saliency Feature Fusion[J].Automotive Engineering, 2024, 46(1): 84-91.

Figures/Tables 12

References 20

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模型	参数量/M	计算量/M	FPS/（帧·s^-1）
YOLOv5	7.03	16.0	62.51
本文算法	7.40	17.0	60.32

模型	P/%	R/%	mAP@0.5/%	mAP@0.5∶0.95/%
YOLOv5	91.5	80.7	88.9	61.4
本文算法	94.5	86.9	92.7	66.6

算法	改进算法				mAP@ 0.5/%	FPS/ （帧·s^-1）
算法	A	B	C	D	mAP@ 0.5/%	FPS/ （帧·s^-1）
YOLOv5					88.9	62.51
算法1	√				93.0	45.70
算法2		√			86.7	90.10
算法3			√		90.1	63.50
算法4				√	90.8	60.60
本文算法	√	√	√	√	92.7	60.32

模型	mAP@0.5/%	FPS/（帧·s^-1）
Faster R-CNN	78.7	12.49
SSD	67.3	37.02
YOLOv3	75.6	56.78
YOLOv4	83.6	60.29
YOLOX	85.3	61.50
TPH-YOLOv5	89.6	58.30
本文算法	92.7	60.32

Lightweight Object Detection Algorithm Based on Image Saliency Feature Fusion

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