Ship Target Detection in SAR Images Based on Improved YOLOv5 and Edge Deployment on Huawei Ascend
Shengrong Qi, Zekang Fan, Zhongzhen Sun, Kefeng Ji · Journal of Physics Conference Series · 2025
Abstract Aiming at the challenges faced in ship target detection in complex large scenes of spaceborne SAR images, such as difficulties in detecting small targets, severe target occlusion, large amounts of SAR data, and limited computing power of airborne/spaceborne computing units, this paper proposes a lightweight ship detection model based on the improved YOLOv5 and realizes the edge deployment of the algorithm in combination with the Huawei Ascend hardware platform. The main contributions include: 1) Rotating target detection framework: By introducing an angle classification head, the problem of bounding box overlap of densely distributed ships near the shore is solved, and the ability to distinguish multi-directional targets is improved. 2) Lightweight network design: Using MobileNetV3 as the backbone network and combining it with a multi-granularity feature enhancement strategy, while compressing the number of model parameters, the detection performance of small targets is maintained. 3) Deployment optimization on the Ascend platform: Reconstructing operators based on the CANN heterogeneous computing architecture, realizing the pipeline optimization of the entire process of preprocessing-inference-post-processing, and achieving real-time detection efficiency on the Atlas 200DK chip. Experiments show that the average precision of the improved model on the public dataset SSDD is 1.74% higher than that of the baseline YOLOv5, and the number of parameters is reduced by 43%, providing a reusable technical path for the self-controllable edge computing of SAR ship detection.