改进YOLOv7的水下目标检测算法

doi:10.3778/j.issn.1002-8331.2309-0072

摘要/Abstract

摘要： 水下目标检测是海洋探测开发过程中一项具有挑战性的任务。针对现有的水下目标检测算法由于水下图像的低可见度和颜色失真等问题导致水下目标检测效果不佳的问题，提出了一种改进YOLOv7的水下目标检测算法，旨在提升水下目标检测性能。设计了一种多信息流融合注意力机制（spatial group-wise coordinated competitive attention， SGCA），解决卷积过程中由于图像全局上下文信息丢失而导致特征丢失的问题，提高了模型在图像模糊情况下的检测精度；并利用switchable atrous convolution（SAConv）模块替换ELAN结构中的3×3卷积模块，以增强骨干网络的特征提取能力。在预测部分采用Wise-IoU作为损失函数，Wise-IoU通过平衡不同质量图像上的模型训练结果，获得更准确的检测结果。采用基于暗通道先验（dark channel prior，DCP）和深度传输图的水下图像增强方法对水下数据集图像进行增强。实验结果表明，改进后的算法在自建的水下目标检测数据集上mAP取得了87.3%，与原始的YOLOv7算法相比较，mAP提高了3.4个百分点；在增强后的水下图像数据集上的mAP为87.1%，提升了2.1个百分点。因此，提出的策略在水下目标检测任务中具有优越的性能。

关键词: YOLOv7, 注意力机制, 水下图像增强, 损失函数

Abstract: Underwater object detection is a challenging task in the process of marine exploration and development. Addressing the issue of poor underwater target detection performance in existing algorithms due to problems such as low visibility and color distortion in underwater images, an improved YOLOv7 underwater target detection algorithm is proposed with the aim of improving underwater object detection performance. Firstly, a multi-information flow fusion attention mechanism (spatial group-wise coordinated competitive attention, SGCA) is designed to solve the problem of feature loss caused by the loss of global context information of the image in the convolution process. It improves the detection accuracy of the model in the case of image blur. Additionally, the switchable atrous convolution (SAConv) module is used to replace the 3×3 convolution module in the ELAN structure to enhance the feature extraction capability of the backbone network. Secondly, Wise-IoU is used as the loss function in the prediction part, which obtains more accurate detection results by balancing model training outcomes on images of varying quality. Finally, an underwater image enhancement method based on dark channel prior (DCP) and depth transmission maps is employed to enhance the images in the underwater dataset. Experimental results show that the improved algorithm achieves a mAP of 87.3% on the self-built underwater object detection dataset, which is 3.4 percengtage points higher than that of the original YOLOv7 algorithm. On the enhanced underwater image dataset, mAP is 87.1%, increases 2.1 percentage points. Therefore, the proposed approach exhibits superior performance in underwater object detection tasks.

Key words: YOLOv7, attention mechanism, underwater image enhancement, loss function

梁秀满, 李然, 于海峰, 刘振东. 改进YOLOv7的水下目标检测算法[J]. 计算机工程与应用, 2024, 60(6): 89-99.

LIANG Xiuman, LI Ran, YU Haifeng, LIU Zhendong. Improved Underwater Object Detection Algorithm of YOLOv7[J]. Computer Engineering and Applications, 2024, 60(6): 89-99.

参考文献

[1] 方俊, 邱春荣.水下图像目标检测技术研究[J].科技资讯, 2022, 20(12): 26-29.
FANG J, QIU C R. Research on underwater image target detection technology[J]. Science & Technology Information, 2022, 20(12): 26-29.
[2] 葛锡云, 魏柠阳, 周宏坤, 等.基于侧扫声呐的水下小目标检测技术研究[J].数字海洋与水下攻防, 2023, 6(2): 155-161.
GE X Y, WEI N Y, ZHOU H K, et al. Research on small underwater target detection technology based on side-scan sonar[J]. Digital Ocean & Underwater Warfare, 2023, 6(2): 155-161.
[3] 张阳婷, 黄德启, 王东伟, 等.基于深度学习的目标检测算法研究与应用综述[J].计算机工程与应用, 2023, 59(18): 1-13.
ZHANG Y T, HUANG D Q, WANG D W, et al. Review on research and application of deep learning-based target detection algorithms[J]. Computer Engineering and Applications, 2023, 59(18): 1-13.
[4] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
[5] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[6] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2117-2125.
[7] LIU Z, DU J, TIAN F, et al. MR-CNN: a multi-scale region-based convolutional neural network for small traffic sign recognition[J]. IEEE Access, 2019, 7: 57120-57128.
[8] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[9] DU L, LI L, WEI D, et al. Saliency-guided single shot multibox detector for target detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(5): 3366-3376.
[10] XIN F F, ZHANG H P, PAN H G. Hybrid dilated multilayer faster RCNN for object detection[J]. The Visual Computer, 2023: 1-14.
[11] 杨婷, 高武奇, 王鹏, 等.自动色阶与双向特征融合的水下目标检测算法[J].激光与光电子学进展, 2023, 60(6): 132-143.
YANG T, GAO W Q, WANG P, et al. Underwater target detection algorithm based on automatic color level and bidirectional feature fusion [J]. Laser & Optoelectronics Progress, 2023, 60(6): 132-143.
[12] 陶洋, 赵文博, 钟邦乾, 等. 融合大卷积核注意力机制的水下目标检测算法[J/OL].小型微型计算机系统: 1-8[2023-10-10].http: //kns.cnki.net/kcms/detail/21.1106.TP.20230915.
1456.046.html.
TAO Y, ZHAO W B, ZHONG B Q, et al. Underwater object detection algorithm with large kernel convolutional attention mechanism[J/OL]. Journal of Chinese Computer Systems, 1-8[2023-10-10]. http: //kns.cnki.net/kcms/detail/21.1106. TP.20230915.1456.046.html.
[13] HOU C, GUAN Z, GUO Z, et al. An improved YOLOv5s-based scheme for target detection in a complex underwater environment[J]. Journal of Marine Science and Engineering, 2023, 11(5): 1041.
[14] YANG Y, CHEN L, ZHANG J, et al. UGC-YOLO: underwater environment object detection based on YOLO with a global context block[J]. Journal of Ocean University of China, 2023, 22(3): 665-674.
[15] SUN Y, ZHENG W, DU X, et al. Underwater small target detection based on YOLOX combined with MobileViT and double coordinate attention[J]. Journal of Marine Science and Engineering, 2023, 11(6): 1178.
[16] LIU K, SUN Q, SUN D, et al. Underwater target detection based on improved YOLOv7[J]. Journal of Marine Science and Engineering, 2023, 11(3): 677.
[17] YU G, CAI R, SU J, et al. U-YOLOv7: a network for underwater organism detection[J]. Ecological Informatics, 2023, 75: 102108.
[18] YU H, LI X, LOU Q, et al. Underwater image enhancement based on DCP and depth transmission map[J]. Multimedia Tools and Applications, 2020, 79: 20373-20390.