Improved YOLOv7 Algorithm for Low-resolution Ship Object Detection in Complex Backgrounds#br#

doi:10.3969/j.issn.1006-2475.2023.11.019

Abstract

Abstract: Abstract: In response to the problems of low resolution target detection and interference from complex backgrounds in ship image target detection， an improved YOLOv7 algorithm is proposed for identifying ship targets. The algorithm is mainly improved in three aspects: using K-means++ algorithm for anchor box clustering in the ship target dataset to obtain anchor box information that is more suitable for ship detection tasks; improving the loss function by using EIOU loss instead of CIOU loss and using Focal loss combined with ɑ-Balanced instead of standard cross-entropy loss; improving the network structure by adding the SPD-Conv module to enhance the detection effect for low-resolution targets. Experimental results show that compared with the original YOLOv7 algorithm， the improved algorithm has an accuracy improvement of 4.22 percentage points， a recall rate improvement of 2.68 percentage points， a mAP@0.5 improvement of 4.3 percentage points， and a detection speed improvement of 2 frames/s. The algorithm achieves good detection results for ship targets.

Key words: Key words: object detection, ship detection, YOLOv7, complex background, low resolution

CLC Number:

TP391

YAN Zi-xian, DONG Bao-liang, TANG Si-mi. Improved YOLOv7 Algorithm for Low-resolution Ship Object Detection in Complex Backgrounds#br#[J]. Computer and Modernization, 2023, 0(11): 120-126.

References ［32］

［1］	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.
［2］	GIRSHICK R. Fast R-CNN［C］// Proceedings of the IEEE International Conference on Computer Vision. 2015:1440-1448.
［3］	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..
［4］	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.
［5］	REDMON J， FARHADI A. YOLO9000: Better， faster， stronger［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:7263-7271.
［6］	REDMON J， FARHADI A. YOLOv3: An incremental improvement［J］. arXiv preprint arXiv:1804.02767， 2018.
［7］	BOCHKOVSKIY A， WANG C Y， LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection［J］. arXiv preprint arXiv:2004.10934， 2020.
［8］	WANG C Y， BOCHKOVSKIY A， LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors［J］. arXiv preprint arXiv:2207.02696， 2022.
［9］	CHANG Y L， ANAGAW A， CHANG L， et al. Ship detection based on YOLOv2 for SAR imagery［J］. Remote Sensing， 2019，11（7）. DOI: 10.3390/rs11070786.
［10］	于洋，李世杰，陈亮，等. 基于改进 YOLO v2 的船舶目标检测方法［J］. 计算机科学， 2019，46（8）:332-336.
［11］	CHEN L Q， SHI W X， DENG D X. Improved YOLOv3 based on attention mechanism for fast and accurate ship detection in optical remote sensing images［J］. Remote Sensing， 2021，13（4）. DOI: 10.3390/rs13040660.
［12］	公明，刘妍妍，李国宁. 改进 YOLO-v3 的遥感图像舰船检测方法［J］. 电光与控制， 2020，27（5）:102-107.
［13］	HE K M， ZHANG X Y， REN S Q， et al. Spatial pyramid pooling in deep convolutional networks for visual recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2015，37（9）:1904-1916.
［14］	SZEGEDY C， VANHOUCKE V， IOFFE S， et al. Rethinking the inception architecture for computer vision［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:2818-2826.
［15］	JIANG J H， FU X J， QIN R， et al. High-speed lightweight ship detection algorithm based on YOLO-V4 for three-channels RGB SAR image［J］. Remote Sensing， 2021，13（10）. DOI: 10.3390/rs13101909.
［16］	ZHENG J C， SUN S D， ZHAO S J. Fast ship detection based on lightweight YOLOv5 network［J］. IET Image Processing， 2022，16（6）:1585-1593.
［17］	谭显东，彭辉. 改进 YOLOv5 的 SAR 图像舰船目标检测［J］. 计算机工程与应用， 2022，58（4）:247-254.
［18］	HOU Q B， ZHOU D Q， FENG J S. Coordinate attention for efficient mobile network design［C］// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021:13713-13722.
［19］	LI S， FU X， DONG J. Improved ship detection algorithm based on YOLOX for SAR outline enhancement image［J］. Remote Sensing， 2022，14（16）.DOI: 10.3390/rs14164070.
［20］	GE Z， LIU S T， WANG F， et al. YOLOx: Exceeding YOLO series in 2021［J］. arXiv preprint arXiv:2107.08430， 2021.
［21］	LIN T Y， MAIRE M， BELONGIE S， et al. Microsoft COCO: Common objects in context［C］// Proceedings of the 13th European Conference on Computer Vision（ECCV 2014）. 2014:740-755.
［22］	ARTHUR D， VASSILVITSKII S. K-means++ the advantages of careful seeding［C］// Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms. 2007:1027-1035.
［23］	ZHENG Z H， WANG P， LIU W， et al. Distance-IoU loss: Faster and better learning for bounding box regression［C］// Proceedings of the AAAI Conference on Artificial Intelligence. 2020，34（7）:12993-13000.
［24］	ZHANG Y F， REN W， ZHANG Z， et al. Focal and efficient IOU loss for accurate bounding box regression［J］. Neurocomputing， 2022，506:146-157.
［25］	LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection［C］// Proceedings of the IEEE International Conference on Computer Vision. 2017:2980-2988.
［26］	YU J H， JIANG Y N， WANG Z Y， et al. Unitbox: An advanced object detection network［C］// Proceedings of the 24th ACM International Conference on Multimedia. 2016:516-520.
［27］	REZATOFIGHI H， TSOI N， GWAK J Y， et al. Generalized intersection over union: A metric and a loss for bounding box regression［C］// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:658-666.
［28］	SUNKARA R， LUO T. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects［C］// European Conference on Machine Learning and Knowledge Discovery in Databases（ECML PKDD 2022）. Springer， 2023:443-459.
［29］	DI Y H， JIANG Z G， ZHANG H P. A public dataset for fine-grained ship classification in optical remote sensing images［J］. Remote Sensing， 2021，13（4）. DOI: 10.3390/rs13040747.
［30］	CHEN J Q， CHEN K Y， CHEN H， et al. Contrastive learning for fine-grained ship classification in remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022，60. DOI: 10.1109/TGRS.2022.3192256.
［31］	XIONG W， XIONG Z Y， CUI Y Q. An explainable attention network for fine-grained ship classification using remote-sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022，60. DOI:10.1109/TGRS.2022.3162195.
［32］	XIAO Q， LIU B， LI Z Y， et al. Progressive data augmentation method for remote sensing ship image classification based on imaging simulation system and neural style transfer［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2021，14:9176-9186.

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