CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism
-
Published:2024-06-02
Issue:11
Volume:12
Page:1728
-
ISSN:2227-7390
-
Container-title:Mathematics
-
language:en
-
Short-container-title:Mathematics
Author:
Wang Tao1ORCID, Zhang Han2, Jiang Dan1
Affiliation:
1. School of Shipping and Marine Engineering, Chongqing Jiaotong University, Chongqing 400074, China 2. School of Tourism and Media, Chongqing Jiaotong University, Chongqing 400074, China
Abstract
Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model’s adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments.
Funder
Ministry of Education, Industry-University Cooperation Collaborative Education Project Xiamen Municipal Natural Science Foundation Upper-level Project Chongqing Municipal Postgraduate Student Supervisory Team Construction Project
Reference33 articles.
1. Ship Contour Extraction from SAR Images Based on Faster R-CNN and Chan–Vese Model;Jiang;IEEE Trans. Geosci. Remote Sens.,2023 2. Cai, J., Du, S., Lu, C., Xiao, B., and Wu, M. (2023, January 8–11). Obstacle Detection of Unmanned Surface Vessel based on Faster RCNN. Proceedings of the 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS), Wuhan, China. 3. Yang, J.-R., Hao, L.-Y., Liu, Y., and Zhang, Y. (2023, January 8–10). SLT-Net: Enhanced Mask RCNN network for ship long-tailed detection. Proceedings of the 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON), Online. 4. Qi, L., Li, B., Chen, L., Wang, W., Dong, L., Jia, X., Huang, J., Ge, C., Xue, G., and Wang, D. (2019). Ship Target Detection Algorithm Based on Improved Faster R-CNN. Electronics, 8. 5. Liu, Y., Wang, Z., Zhang, F., Xie, J., and Xu, Z. (2022, January 23–25). Target scale information detection based on improved Faster R-CNN. Proceedings of the Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), Shanghai, China. Proc. SPIE 12587, 125870V.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|