侧扫声纳检测沉船目标的轻量化DETR-YOLO法

doi:10.12305/j.issn.1001-506X.2022.08.06

Abstract

Abstract:

Although the side-scan sonar shipwreck target detection method based on the YOLOv5 algorithm has achieved good results in detection accuracy and speed, however, how to further improve the accuracy of small target detection under the background of complex ocean noise, reduce the missed alarm rate and false alarm rate of overlapping targets, and realize the lightweight of the model is an urgent issue to be solved. To this end, this paper innovatively integrates the structure of DETR (end-to-end object detection with transformers) and YOLOv5, and proposes a lightweight side-scan sonar shipwreck detection model based on the DETR-YOLO model. Firstly, a multi-scale feature complex fusion module is added to improve the detection ability of small targets. Then, the attention mechanism SENet (squeeze-and-excitation networks) is integrated strengthen the sensitivity to important channel features. Finally WBF (weighted boxes fusion) weighted fusion frame strategy is adopted to improve the positioning accuracy and confidence of the detection frame. The experimental results show that the AP_0.5 and AP_0.5∶0.95 values of this model in the test set reach 84.5% and 57.7%, respectively, which are greatly improved compared with the Transfermer and YOLOv5a models.At the expense of smaller detection efficiency loss and weight increase, higher detection accuracy has been achieved. At the same time, it can improve the full-scene understanding ability and the small-scale overlapping target processing ability while meeting the needs of lightweight engineering deployment.

Key words: DETR-YOLO model, multi-scale feature complex fusion, weighted boxes fusion (WBF)

CLC Number:

P227

Yulin TANG, Houpu LI, Weidong ZHANG, Shaofeng BIAN, Guojun ZHAI, Min LIU, Xiaoping ZHANG. Lightweight DETR-YOLO method for detecting shipwreck target in side-scan sonar[J]. Systems Engineering and Electronics, 2022, 44(8): 2427-2436.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 1

Table 2

Fig.10

Fig.11

References 32

1	LEHARDY P K, MOORE C. Deep ocean search for Malaysia airlines flight 370[C]//Proc. of the Ocean-ST. JOHN's, 2014.
2	王久, 曹敬涛, 卢秉武, 等. 水下目标探测项目设备应用和发展趋势[J]. 珠江水运, 2016, (11): 43- 44. doi: 10.3969/j.issn.1672-8912.2016.11.021
	WANG J , CAO J T , LU B W , et al. Underwater target detection project equipment application and development trend[J]. China Water Transport, 2016, (11): 43- 44. doi: 10.3969/j.issn.1672-8912.2016.11.021
3	赵建虎, 李娟娟, 李萌. 海洋测量的进展及发展趋势[J]. 测绘信息与工程, 2009, 34 (4): 25- 27.
	ZHAO J H , LI J J , LI M . Progress and future trend of hydrographic surveying and charting[J]. Journal of Geomatics, 2009, 34 (4): 25- 27.
4	陈正荣, 王正虎. 多波束和侧扫声纳系统在海底目标探测中的应用[J]. 海洋测绘, 2013, 33 (4): 51- 54. doi: 10.3969/j.issn.1671-3044.2013.04.014
	CHEN Z R , WANG Z H . Research on underwater target detection using side-scan sonar and multibeam sounding system[J]. Hydrographic Survering and Charting, 2013, 33 (4): 51- 54. doi: 10.3969/j.issn.1671-3044.2013.04.014
5	王久, 周健. 侧扫声纳和多波束系统在失事沉船扫测中的综合应用[J]. 中国水运, 2010, 10 (8): 35- 37. doi: 10.3969/j.issn.1006-7973-C.2010.08.020
	WANG J , ZHOU J . Comprehensive application of side-scan sonar and multi-beam system in shipwreck survey[J]. China Water Transport, 2010, 10 (8): 35- 37. doi: 10.3969/j.issn.1006-7973-C.2010.08.020
6	刘陈. 多波束系统、侧扫声纳与磁力仪在海底沉船探测中的比较分析[D]. 北京: 中国地质大学, 2015.
	LIU C. The comparative analysis of multi-beam sounding system, side-scan sonar and magnetometer in the wreck detection[D]. Beijing: China University of Geosciences, 2015.
7	朱建军, 宋迎春, 胡俊, 等. 测绘大数据时代数据处理理论面临的挑战与发展[J]. 武汉大学学报(信息科学版), 2021, 46 (7): 1025- 1031.
	ZHU J J , SONG Y C , HU J , et al. Challenges and development of data processing theory in the era of surveying and mapping big data[J]. Geomatics and Information Science of Wuhan University, 2021, 46 (7): 1025- 1031.
8	赵建虎, 王晓, 张红梅, 等. 侧扫声呐图像分割的中性集合与量子粒子群算法[J]. 测绘学报, 2016, 45 (8): 935- 942.935-942, 951
	ZHAO J H , WANG X , ZHANG H M , et al. The neutrosophic set and quantum-behaved particle swarm optimization algorithm of side-scan sonar image segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45 (8): 935- 942.935-942, 951
9	王晓. 侧扫声呐图像精处理及目标识别方法研究[D]. 武汉: 武汉大学, 2017.
	WANG X. Research on precise processing of side scan sonar image and object recognition methods[D]. Wuhan: Wuhan University, 2017.
10	朱殿尧, 卞红雨. 侧扫声纳目标自动探测研究[J]. 吉林大学学报(信息科学版), 2008, 26 (6): 627- 631. doi: 10.3969/j.issn.1671-5896.2008.06.014
	ZHU D Y , BIAN H Y . Research of side-scan sonar target auto detection[J]. Journal of Jilin University (Information Science Edition), 2008, 26 (6): 627- 631. doi: 10.3969/j.issn.1671-5896.2008.06.014
11	李海滨, 滕惠忠, 宋海英, 等. 基于侧扫声纳图像海底目标物提取方法[J]. 海洋测绘, 2010, 30 (6): 71- 73.
	LI H B , TENG H Z , SONG H Y , et al. Technology on the extraction of seabed target based on high resolution side-scan sonar[J]. Hydrographic Survering and Charting, 2010, 30 (6): 71- 73.
12	阳凡林, 独知行, 吴自银, 等. 基于灰度直方图和几何特征的声纳图像目标识别[J]. 海洋通报, 2006, (5): 64- 69. doi: 10.3969/j.issn.1001-6392.2006.05.010
	YAN F L , DU Z X , WU Z Y , et al. Object recognizing on sonar image based on histogram and geometric feature[J]. Marine Science Bulletin, 2006, (5): 64- 69. doi: 10.3969/j.issn.1001-6392.2006.05.010
13	吕良, 周超烨, 陈春, 等. 基于虚警函数的侧扫声纳水下目标实时检测方法[J]. 海洋测绘, 2013, 33 (4): 35- 38. doi: 10.3969/j.issn.1671-3044.2013.04.009
	LYU L , ZHOU C Y , CHEN C , et al. Real-time detection of underwater target using side-scan sonar based on false alarm function[J]. Hydrographic Surveying and Charting, 2013, 33 (4): 35- 38. doi: 10.3969/j.issn.1671-3044.2013.04.009
14	朱殿尧, 卞红雨. 侧扫声纳目标自动探测研究[J]. 吉林大学学报(信息科学版), 2008, 26 (6): 627- 631. doi: 10.3969/j.issn.1671-5896.2008.06.014
	ZHU D Y , BIAN H Y . Research of side-scan sonar target auto detection[J]. Journal of Jilin University (Information Science Edition), 2008, 26 (6): 627- 631. doi: 10.3969/j.issn.1671-5896.2008.06.014
15	陈强. 基于水声图像水下目标识别的技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.
	CHEN Q. Research-based underwater acoustic images underwater target recognition[D]. Harbin: Harbin Engineering University, 2012.
16	续元君. 水下目标探测关键技术研究[D]. 大连: 大连海事大学, 2011.
	XU Y. Research on the key technologies of detection underwater[D]. Dalian: Dalian Maritime University, 2011.
17	汤寓麟, 金绍华, 边刚, 等. 侧扫声呐识别沉船影像的迁移学习卷积神经网络法[J]. 测绘学报, 2021, 50 (2): 260- 269.
	TANG Y L , JIN S H , BIAN G , et al. The transfer learning with convolutional neural network method of side-scan sonar to identify wreck images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50 (2): 260- 269.
18	TANG Y L, JIN S H, BIAN G, et al. Wreckage target recognition in side-scan sonar images based on an improved faster R-CNN model[C]//Proc. of the International Conference on Big Data & Artificial Intelligence & Software Engineering, 2020: 348-354.
19	TANG Y L , JIN S H , BIAN G , et al. Shipwreck target recognition in side-scan sonar images by improved YOLOv3 model based on transfer learning[J]. IEEE Access, 2020, 8, 173450- 173460. doi: 10.1109/ACCESS.2020.3024813
20	汤寓麟, 边少锋, 翟国君, 等. 侧扫声纳检测沉船目标的改进YOLOv5法[EB/OL]. [2021-09-01]. https://doi.org/10.13203/j.whugis20210353.
	TANG Y L, BIAO S F, ZHAI G J, et al. Improved YOLOv5 method for detecting shipwreck target with side-scan sonar[EB/OL]. [2021-09-01]. https://doi.org/10.13203/j.whugis20210353.
21	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proc. of the Advances in neural Information Processing Systems, 2017: 5998-6008.
22	FEDUS W, ZOPH B, SHAZEER N. Switch transformers: scaling to trillion parameter models with simple and efficient sparsity[EB/OL]. [2021-09-01]. https://arXiv.org/abs/2101.03961.
23	PRANGEMEIER T, REICH C, KOEPPL H. Attention-based transformers for instance segmentation of cells in microstructures[C]//Proc. of the IEEE International Conference on Bioinformatics and Biomedicine, 2020: 700-707.
24	YANG F, YANG H, FU J, et al. Learning texture transformer network for image super-resolution[C]//Proc. of the Computer Vision and Pattern Ecognition, 2020.
25	CHEN H, WANG Y, GUO T, et al. Pre-trained image processing transformer[EB/OL]. [2021-09-01]. https//arXiv.org/abs/2012.00364.
26	ZHANG H, GOODFELLOW I, METAXAS D, et al. Self attention generative adversarial networks[C]//Proc. of the International Conference on Machine Learning, 2019: 7354-7363.
27	CHEN C F, FAN Q, PANDA R. Crossvit: cross-attention multiscale vision transformer for image classification[EB/OL]. [2021-09-01]. https//arXiv.org/abs/2103.14899.
28	CHEN C F, PANDA R, FAN Q. Regionvit: regional-to-local attention for vision transformers[EB/OL]. [2021-09-01]. https//arXiv.org/abs/2106.02689.
29	DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[C]//Proc. of the Computer Vision and Pattern Recongnition, 2021.
30	CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//Proc. of the European Conference on Computer Vision, 2020.
31	范习健, 李庆武, 黄河, 等. 侧扫声呐图像的3维块匹配降斑方法[J]. 中国图象图形学报, 2012, 17 (1): 68- 74.
	FAN X J , LI Q W , HUANG H , et al. Side-scan sonar image despecking based on block-matching and 3D filtering[J]. Journal of Image and Graphics, 2012, 17 (1): 68- 74.
32	HELLEQUIN L , BOUCHER J M , LURTON X . Processing of high-frequency multibeam echo sounder data for seafloor cha-racterization[J]. IEEE Journal of oceanic Engineering, 2003, 28 (1): 78- 89.

模型	AP_0.5/%	AP_0.5~0.95/%	FPS	权重/MB
Transformer	77.3	43.9	442	15.3
YOLOv5a	81.8	51.6	430	18.6
DETR-YOLO	84.5	57.7	427	20.1

组别	DETR	多尺度特征复融合	SENet	AP_0.5/%	FPS
1	√	－	－	83.16	441
2	√	√	－	84.01	429
3	√	－	√	83.57	438
4	√	√	√	84.52	427

Lightweight DETR-YOLO method for detecting shipwreck target in side-scan sonar

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 32

Related Articles 0

Recommended Articles

Metrics

Comments