改进YOLOv7的轻量化水下目标检测算法

doi:10.3778/j.issn.1002-8331.2308-0333

摘要/Abstract

摘要： 针对水下设备内存和计算能力有限和水下环境复杂造成的目标错检和漏检问题，提出一种轻量级水下目标检测方法YOLOv7-SDBB。在YOLOv7的骨干网络上引入ShuffleNetv2轻量级网络，降低特征提取网络的参数量和计算量；设计了D-ELAN和D-MPConv模块，在进一步实现网络轻量化的同时提高模型检测速度；由于水下检测过程中容易出现错检、漏检的现象，利用BiFPN（bidirectional feature pyramid network）进行多尺度特征融合，融合深层的特征信息；针对BiFPN特征融合导致的特征信息丢失的问题，采用BiFormer注意力机制保留关键信息，提高目标检测精度。实验结果表明，改进后模型在URPC2020数据集上的精度提高了2.7个百分点，参数量和计算量分别下降了20.3%和41.7%，检测速度提升至100.9?FPS，从而验证了提出的算法在精度和速度之间取得了很好的平衡。

关键词: 轻量级网络, 水下目标检测, YOLOv7, 稀疏注意力机制

Abstract: Aiming at the problems of target false and missing detection caused by limited memory and computing power of underwater equipment and complex underwater environment, a lightweight underwater target detection method YOLOv7-SDBB is proposed. The ShuffleNetv2 lightweight network is introduced on the backbone network of YOLOv7 to reduce the parameter amount and calculation amount of the feature extraction network. The D-ELAN and D-MPConv modules are designed to further realize the network lightweight and improve the model detection speed. Due to the phenomenon of false and missing detection is prone to occur during underwater detection, BiFPN is used to perform multi-scale feature fusion and integrate deep feature information. In view of the problem of feature information loss caused by BiFPN feature fusion, the BiFormer attention mechanism is used to retain key information and improve target detection accuracy. The experimental results show that the accuracy of the improved model on the URPC2020 dataset has increased by 2.7 percentage points, the amount of parameters and calculations have decreased by 20.3% and 41.7% respectively, and the detection speed has increased to 100.9?FPS, realizing a good balance between the speed and accuracy of underwater target detection.

Key words: lightweight network, underwater target detection, YOLOv7, sparse attention mechanism

辛世澳, 葛海波, 袁昊, 杨雨迪, 姚洋. 改进YOLOv7的轻量化水下目标检测算法[J]. 计算机工程与应用, 2024, 60(3): 88-99.

XIN Shi’ao, GE Haibo, YUAN Hao, YANG Yudi , YAO Yang. Improved Lightweight Underwater Target Detection Algorithm of YOLOv7[J]. Computer Engineering and Applications, 2024, 60(3): 88-99.

参考文献

[1] ZHOU X, DING W, JIN W. Microwave-assisted extraction of lipids, carotenoids, and other compounds from marine resources[M//Innovative and emerging technologies in the bio-marine food sector.[S.l.]: Academic Press, 2022: 375-394.
[2] CHRISTENSEN L, DE GEA FERNáNDEZ J, HILDEBRANDT M, et al. Recent advances in AI for navigation and control of underwater robots[J]. Current Robotics Reports, 2022, 3(4): 165-175.
[3] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436.
[4] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
[5] REN S, HE K, GIRSHICK R, et al. Faster RCNN: towards realtime object detection with region proposal networks[C]//Proceedings of Advances in Neural Information Processing Systems, 2015: 91-99.
[6] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[7] 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.
[8] ZHANG M, XU S, SONG W, et al. Lightweight underwater object detection based on YOLO v4 and multi-scale attentional feature fusion[J]. Remote Sensing, 2021, 13(22): 4706.
[9] CHEN X, YUAN M, YANG Q, et al. Underwater-YCC: underwater target detection optimization algorithm based on YOLOv7[J]. Journal of Marine Science and Engineering, 2023, 11(5): 995.
[10] LI Y, BAI X, XIA C. An improved YOLOV5 based on triplet attention and prediction head optimization for marine organism detection on underwater mobile platforms[J]. Journal of Marine Science and Engineering, 2022, 10(9): 1230.
[11] 叶赵兵, 段先华, 赵楚. 改进YOLOv3-SPP水下目标检测研究[J]. 计算机工程与应用, 2023, 59(6): 231-240.
YE Z B, DUAN X H, ZHAO C. Research on underwater target detection by improved YOLOv3-SPP[J]. Computer Engineering and Applications, 2023, 59(6): 231-240.
[12] WANG C Y, BOCHKOVSKIY A, LIAO H. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv:2207.02696, 2022.
[13] MA N, ZHANG X, ZHENG H T, et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[J]. arXiv:1807.11164, 2018.
[14] HOWARD A, ZHU M L, CHEN B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017.
[15] TAN M, PANG R, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 10778-10787.
[16] ZHU L, WANG X, KE Z, et al. BiFormer: vision transformer with bi-level routing attention[J]. arXiv:2303.08810, 2023.
[17] DING X H, ZHANG X Y, MA N N, et al. RepVGG: making VGG style ConvNets great again[J]. arXiv:2101.03697, 2021.
[18] DOLLáR P, SINGH M, GIRSHICK R. Fast and accurate model scaling[J]. arXiv:2103.06877, 2021.
[19] BIAN P, ZHENG Z, ZHANG D. Light-weight multi-channel aggregation network for image super-resolution[C]//Chinese Conference on Pattern Recognition and Computer Vision, 2021.
[20] 楚玉春, 龚航, 王学芳, 等. 基于 YOLOv4 的目标检测知识蒸馏算法研究[J]. 计算机科学, 2022, 49(6A): 337-344.
CHU Y C, GONG H, WANG X F, et al. Study on konwledge distillation of target detection algorithm based on YOLOV4[J]. Computer Science, 2022, 49(6A): 337-344.
[21] NIU Z Y, ZHONG G Q, YU H. A review on the attention mechanism of deep learning[J]. Neurocomputing, 2021, 452: 48-62.
[22] ZHANG D, ZHENG Z, LI M, et al. CSART: channel and spatial attention-guided residual learning for real-time object tracking[J]. Neurocomputing, 2023, 436: 260-272.
[23] LIU Z, LIN Y, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 10012-10022.
[24] LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, 2018: 8759-8768.
[25] GUO Y, CHEN S, ZHAN R, et al. SAR ship detection based on YOLOv5 using CBAM and BiFPN[C]//IGARSS 2022—2022 IEEE International Geoscience and Remote Sensing Symposium, 2022: 2147-2150.
[26] CHEN J, MAI H S, LUO L, et al. Effective feature fusion network in BIFPN for small object detection[C]//2021 IEEE International Conference on Image Processing (ICIP), 2021: 699-703.
[27] 李翔, 张涛, 张哲, 等. Transformer在计算机视觉领域的研究综述[J]. 计算机工程与应用, 2023, 59(1): 1-14.
LI X, ZHANG T, ZHANG Z, et al. Survey of Transformer research in computer vision[J]. Computer Engineering and Applications, 2023, 59(1): 1-14.
[28] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[29] WANG Q L, WU B G, ZHU P F. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, June 13-19, 2020. Piscataway, NJ: IEEE, 2020: 11531-11539.
[30] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.
[31] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[32] PAN X, GE C, LU Ｒ, et al. On the integration of self-attention and convolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 815-825.
[33] XI D, QIN Y, WANG S. YDRSNet: an integrated YOLOv5-Deeplabv3+real-time segmentation network for gear pitting measurement[J]. Journal of Intelligent Manufacturing, 2023, 34: 1585-1599.
[34] TIAN Z, SHEN C, CHEN H, et al. Fcos: fully convolutional one-stage object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2019: 9627-9636.
[35] GE Z, LIU S, WANG F, et al. Yolox: exceeding yolo series in 2021[J]. arXiv:2107.08430, 2021.