A Lightweight Ship Target Detection Method based on Improved YOLOv8
DOI:
https://doi.org/10.6919/ICJE.202412_10(12).0009Keywords:
YOLOv8; Object Detection and Recognition; Attention Mechanism; Ships.Abstract
In the realm of modern maritime and inland shipping management, the demands for sophisticated oversight are continually escalating. With the seamless fusion of deep learning and computer vision, ship target detection has emerged as a pivotal detection method within shipping management. To address the challenges posed by varying ship sizes and high quantities in complex scenarios, where traditional computer vision methods suffer from inadequate detection accuracy and sluggish recognition speeds, this paper introduces a lightweight ship target detection approach, CL-YOLO, based on an improved YOLOv8 model. Initially, we integrate the Coordinate Attention mechanism with the C2f module, decoupling and optimizing the channel attention mechanism. By individually fusing features across two distinct spatial dimensions, we devise an efficient one-dimensional feature encoding strategy that alleviates the model's computational burden. Furthermore, to enhance the model's detection efficiency, we incorporate the Large Kernel Attention (LKA) mechanism into the Backbone, significantly boosting detection accuracy without altering the parameter count. Upon experimenting with the optimized model on the SeaShips dataset, we observed an 1.1% increase in mean Average Precision (mAP) compared to the unoptimized YOLOv8, achieving a remarkable 97.5%. This underscores CL-YOLO's superior accuracy in detecting inland ships.
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