Systems Engineering and Electronics ›› 2022, Vol. 44 ›› Issue (6): 1823-1832.doi: 10.12305/j.issn.1001-506X.2022.06.07
• Electronic Technology • Previous Articles Next Articles
Kun QIAN1,2,*, Chenxuan LI1, Meishan CHEN1, Yao WANG1
Received:
2021-07-16
Online:
2022-05-30
Published:
2022-05-30
Contact:
Kun QIAN
CLC Number:
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.
Table 1
Depth and width multiple parameters of different YOLOv5 network structures"
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 |
Table 4
Detection accuracy and speed of several algorithms on data set of key parts of ship"
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[ | 500×500 | 64.82 | 88.4 | 70.2 | 73.9 | 52.6 | 62.8 | 66.3 | 39.5 | 12 |
SSD[ | 500×500 | 72.34 | 94.1 | 77.6 | 81.9 | 64.4 | 69.7 | 64.8 | 53.9 | 28 |
Mask R-CNN[ | 512×384 | 73.65 | 96.2 | 78.9 | 83.1 | 66.3 | 72.5 | 63.7 | 54.8 | 14 |
CenterNet-Hourglass(simple)[ | 512×512 | 72.25 | 94.6 | 77.2 | 81.4 | 62.3 | 68.5 | 63.4 | 51.3 | 26 |
改进YOLOv2[ | 416×416 | 72.20 | 96.8 | 77.5 | 80.2 | 62.1 | 67.2 | 64.5 | 50.1 | 24 |
YOLOv4[ | 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 |
1 | KIM Y. Convolutional neural networks for sentence classification[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1408.5882v2. |
2 | DALAL N. Histograms of oriented gradients for human detection[C]//Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886-893. |
3 | 王瑶, 徐昌, 舒福舟. 基于SVM算法的两种特征提取的图像分类方法分析[J]. 电脑与信息技术, 2019, 27 (6): 18- 20.18-20, 33 |
WANG Y , XU C , SHU F Z . Analysis of image classification methods based on two feature extraction based on SVM algorithm[J]. Computer and Information Technology, 2019, 27 (6): 18- 20.18-20, 33 | |
4 | ZHU J , ARBOR A , HASTIE T . Multi-class adaBoost[J]. Statistics and its Interface, 2006, 2 (3): 349- 360. |
5 | 苏赋, 吕沁, 罗仁泽. 基于深度学习的图像分类研究综述[J]. 电信科学, 2019, 35 (11): 58- 74. |
SU B , LYU Q , LUO R Z . Review of image classification based on deep learning[J]. Telecommunications Science, 2019, 35 (11): 58- 74. | |
6 | ZOU Z X, SHI Z W, GUO Y H, et al. Object detection in 20 years: a survey[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1905.05055v2. |
7 | HINTON G E , OSINDERO S , TEH Y W . A fast learning algorithm for deep belief nets[J]. Neural Computation, 2014, 18 (7): 1527- 1554. |
8 | GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587. |
9 | 南晓虎, 丁雷. 深度学习的典型目标检测算法综述[J]. 计算机应用研究, 2020, 37 (S2): 15- 21. |
NAN X H , DING L . Review of typical target detection algorithms based on deep learning[J]. Application Research of Computers, 2020, 37 (S2): 15- 21. | |
10 | GIRSHICK R. Fast R-CNN[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1504.08083. |
11 | REN S Q , HE K M , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans.on Pattern Analysis & Machine Intelligence, 2017, 39 (6): 1137- 1149. |
12 | CHEN X L, GUPTA A. An implementation of faster RCNN with study for region sampling[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1702.02138v2. |
13 | DAI J F, LI Y, HE K M, et al. R-FCN: object detection via region-based fully convolutional networks[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1605.06409v2. |
14 | SINGH B, LI H D, SHARMA A, et al. R-FCN-3000 at 30fps: decoupling detection and classification[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1712.01802v1. |
15 | HE K M, GKIOXARI G, P DOLLAR, et al. Mask R-CNN[C]// Proc. of the IEEE International Conference on Computer Vision, 2017: 2980-2988. |
16 | LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multiBox detector[C]//Proc. of the European Conference on Computer Vision, 2020, 42(2): 318-327. |
17 | LIN T Y , GOYAL P , GIRSHICK R , et al. Focal loss for dense object detection[J]. IEEE Trans.on Pattern Analysis & Machine Intelligence, 2020, 42 (2): 318- 327. |
18 | REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788. |
19 | REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6517-6525. |
20 | YU H, ZHANG Z, QIN Z N, et al. Loss rank mining: a general hard example mining method for real-time detectors[C]//Proc. of the International Joint Conference on Neural Networks, 2018. |
21 | REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1804.02767v1. |
22 | BOCHKOVSKIV A, WANG C Y, LIAO H. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. [2022-04-22]. https://arxiv.org/abs/2004.10934v1. |
23 | 聂丰英. 基于多特征联合稀疏表示的SAR舰船目标识别方法[J]. 火力与指挥控制, 2020, 45 (10): 34- 38. |
NIE F Y . SAR ship target recognition method using joint sparse representation of multiple features[J]. Fire Control & Command Control, 2020, 45 (10): 34- 38. | |
24 | 吴映铮, 杨柳涛. 基于HOG和SVM的船舶图像分类算法[J]. 上海船舶运输科学研究所学报, 2019, 42 (1): 58- 64. |
WU Y Z , YANG L T . Ship image classification by combined use of HOG and SVM[J]. Journal of Shanghai Ship and Shipping Research Institute, 2019, 42 (1): 58- 64. | |
25 | 李兆桐, 孙浩云. 基于全卷积神经网络的船舶检测和船牌识别系统[J]. 计算机与现代化, 2019, (12): 72- 77. |
LI Z T , SUN H Y . A ship detection and plate recognition system based on FCN[J]. Computer and Modernization, 2019, (12): 72- 77. | |
26 | 段敬雅. 基于深度学习的船舶目标识别算法研究[D]. 广州: 华南理工大学, 2020. |
DUAN J Y. Research on ship recognition algorithm based on deep learning[D]. Guangzhou: South China University of Technology, 2020. | |
27 | 曲颖丽. 基于卷积神经网络的船舶识别[D]. 大连: 大连海事大学, 2020. |
QU Y L. Ship recognition based on convolutional neural network[D]. Dalian: Dalian Maritime University, 2020. | |
28 | HE K M , ZHANG X Y , REN S Q , et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Trans.on Pattern Analysis & Machine Intelligence, 2014, 37 (9): 1904- 1916. |
29 | OUYANG W, WANG X Q, ZENG X G, et al. DeepID-net: deformable deep convolutional neural networks for object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2015. |
30 | LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768. |
31 | LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936-944. |
32 | ZEILER M D, FERGUS R. Stochastic pooling for regularization of deep convolutional neural networks[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1301.3557v1. |
33 | 高惠琳. 基于卷积神经网络的军事图像分类[J]. 计算机应用研究, 2017, (11): 323- 325. |
GAO H L . Military image classification based on convolutional neural network[J]. Application Research of Computers, 2017, (11): 323- 325. | |
34 | TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2020. |
35 | CLEVERT D A, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units (ELUs)[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1511.07289v5. |
36 | DUAN K W, BAI S, XIE L X, et al. CenterNet: keypoint triplets for object detection[EB/OL]. [2022-04-22]. https://arxiv.org/abs/1904.08189v3. |
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