基于YOLOv5的舰船目标及关键部位检测算法

doi:10.12305/j.issn.1001-506X.2022.06.07

Abstract

Abstract:

In order to improve the detection and recognition success rate of the surface warship target in visible light images, an algorithm based on YOLOv5 is proposed. The spatial pyramid pooling network based on stochastic pooling is used for pooling operation, and the bi-directional feature pyramid network is used for feature fusion. At the same time, the exponential linear unit function is used as the activation function to further accelerate the convergence speed and improve the robustness of the model, so as to realize the rapid and accurate recognition of surface ship targets and key parts of the ship. Through the experimental verification on the data set of the ship target and its key parts, compared with the mainstream target detection methods, the recognition accuracy is improved in varying degrees. Compared with the original YOLOv5s model, the mean average precision is improved by 3.03%, and the speed is improved by 2 FPS. The model maintains the lightweight characteristics of YOLOv5 and has a good prospect in application deployment.

Key words: YOLOv5, stochastic pooling, bi-directional feature pyramid network, exponential linear unit function

CLC Number:

TP39

Kun QIAN, Chenxuan LI, Meishan CHEN, Yao WANG. Ship target and key parts detection algorithm based on YOLOv5[J]. Systems Engineering and Electronics, 2022, 44(6): 1823-1832.

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参数	YOLOv5s	YOLOv5m	YOLOv5l	YOLOv5x
深度倍数	0.33	0.67	1.0	1.33
宽度倍数	0.50	0.75	1.0	1.25
骨干段CSPN数量	133	266	399	41 212
颈部段CSPN数量	1	2	3	4
骨干段Focus和4个CBL中卷积核数量	3 264 128, 256 512	4 896 192, 384 768	64 128 256, 5 121 024	80 160 320, 6 401 280

标注类别	数据集总数	航空母舰	驱逐舰	桅杆	驾驶舱	相控阵雷达天线	舵机舱	民用船只
数量/幅	1 544	636	741	1 476	1 032	405	393	558
各类占比/%	-	41.2	48.0	95.6	66.8	26.2	25.5	36.1

操作系统	Windows10
CPU	i7-9700 3.00 GHz
GPU	NVIDIA 2060 SUPER
GPU显存/GB	8
内存/G	16
加速环境	CUDAv11.0、CuDNNv7.6
显卡驱动版本	27.21.14.617 2
Pytorch版本	1.7
Python版本	3.8.5

算法	图像分辨率	mAP/%	航母	驱逐舰	桅杆	驾驶舱	天线	舵机舱	民船	FPS
HOG+SVM[24]	500×500	64.82	88.4	70.2	73.9	52.6	62.8	66.3	39.5	12
SSD[16]	500×500	72.34	94.1	77.6	81.9	64.4	69.7	64.8	53.9	28
Mask R-CNN[15]	512×384	73.65	96.2	78.9	83.1	66.3	72.5	63.7	54.8	14
CenterNet-Hourglass(simple)[36]	512×512	72.25	94.6	77.2	81.4	62.3	68.5	63.4	51.3	26
改进YOLOv2[26]	416×416	72.20	96.8	77.5	80.2	62.1	67.2	64.5	50.1	24
YOLOv4[22]	512×512	73.78	97.2	81.7	80.3	66.5	66.1	64.2	53.4	24
YOLOv5s	640×640	74.10	97.2	79.4	84.6	65.9	69.2	67.1	55.3	25
本文算法	640×640	77.13	97.3	86.1	84.7	67.8	79.9	63.9	60.2	27

算法	参数量/M	FLOPs/GB
RetinaNet[17]	53.1	127
Mask R-CNN[15]	63.4	164
CenterNet-Hourglass (simple)[36]	95.4	140
改进YOLOv2[26]	107.7	149
YOLOv4[22]	27.6	31
YOLOv5s	7.5	17
Proposed method	7.3	13.2

Ship target and key parts detection algorithm based on YOLOv5

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实验	随机池化SPP	BiFPN	ELU	mAP/%	FPS
实验1	√	-	-	74.26	25
实验2	-	√	-	74.88	25
实验3	-	-	√	73.95	26
实验4	√	√	-	74.35	25
实验5	-	√	√	75.37	26
实验6	√	-	√	75.92	26
本文算法实验	√	√	√	77.13	27

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