Degrad Target Detection Algorithm

doi:10.12382/bgxb.2023.0180

Abstract

Abstract:

The target detection accuracy in the imaging test phase of armored vehicle dynamic performance assessment is closely related to the precision of weapon equipment identification and qualification. To address the degradation issues such as low target image contrast and poor discernibility, a degraded target detection algorithm based on improved YOLOv5 is proposed. The proposed algorithm utilizes a multi-branch grouping convolutional structure combined with deep and pointwise convolutions to construct a backbone feature extraction network, thus reducing the computational complexity of network parameters and improving the detection speed. The representation attention mechanism is introduced to enhance the representation capability of the targets. At the network output layer, a three-branch spatial feature fusion is introduced to combine the fine-grained feature information from low-level feature maps and the rich semantic information from high-level feature maps, preserving the details and edge semantic information of degraded target images. Experimental results demonstrate that, in the target dataset, the proposed algorithm achieves a detection accuracy of 90.88% in terms of mean average precision (mAP) and a detection speed of 52.74 fps. It can efficiently and accurately complete the target detection phase in the imaging test of dynamic performance assessment.

Key words: target, degradedimage, target detection, feature fusion, attention mechanism

CLC Number:

TJ811

LIU Peng, XIONG Zeyu, JING Wenbo, FENG Xuan, ZHANG Junhao, LIU Tongbo, WU Xueni, XIA Xuan, WAN Linlin, ZHAO Haili. Degrad Target Detection Algorithm[J]. Acta Armamentarii, 2024, 45(6): 2065-2075.

Figures/Tables 19

Fig.1 Single image enhancement

Fig.2 YOLOE network model structure

Fig.3 Deep point-by-point convolution structure

Fig.4 Comparison diagram of convolutional block structures

Fig.5 Schematic representation of attention mechanism

Fig.6 Channel attention sub-module

Fig.7 Spatial attention sub-module

Fig.8 Three-branch adaptive spatial feature fusion

Fig.9 EIoU diagram

Table 1 Initial parameter setting

参数	数值
初始学习率设置	0.005
学习率调整算法	余弦退火算法
训练周期	100epoch
梯度下降优化算法	SGD算法
批量处理样本数量	16
IoU阈值	0.5
置信度阈值	0.5

Fig.10 Anchor frame clustering

Fig.11 Comparison chart of performances of various target detection algorithms

Table 2 Test results of various target detection algorithms

序号	目标检测算法	图片分辨率/像素	mAP/%	F₁/%	FPS/(帧·s)
1	FASTER R-CNN	600×600	79.51	69.81	15.16
2	SSD	600×600	64.91	60.91	25.23
3	NanoDet	320×320	70.12	61.82	60.82
4	Cascade R-CNN	640×640	83.48	80.18	20.18
5	YOLOv5	640×640	83.02	80.71	49.12
6	YOLOE	640×640	90.88	88.92	52.74
7	YOLOX	640×640	86.55	86.12	52.60

Table 3 Comparison of degrad target test results

Table 4 Results of YOLOE model ablation experiments

模型	改进策略	mAP/%	FPS/ (帧·s^-1)	Parameters/ M
A	YOLOv5	83.02	49.12	7.60
B	A+主干网络改进	82.86	56.25	6.34
C	B+表征注意力机制	85.24	54.68	6.52
D	C+三分支自适应空间特征融合	88.91	52.74	7.05
E	D+损失函数改进	90.88	52.74	7.05

Fig.12 Comparison of feature fusion maps

Fig.13 Comparison of the effects of attention mechanisms in the presence of interference

Fig.14 Comparison of feature fusion visualisation effects

Fig.15 Comparison of bounding box loss functions

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