A Lightweight Fine-Grained Detection Algorithm for Ship Critical Components in UAV Maritime Surveillance

Detecting ship critical components in UAV-based maritime monitoring remains challenging because of small-object detail loss, multi-scale semantic misalignment, and limited computational resources on embedded platforms. To address these issues, this study proposes D-MobileNetV3, a lightweight anchor-free detection algorithm based on an enhanced CenterNet framework. In the proposed framework, MobileNetV3 is used as the lightweight backbone, and an RFB module is introduced to improve multi-scale contextual representation. A High-Frequency and Spatial Perception Feature Pyramid Network (HS-FPN) with a serially coupled dual-path architecture is then constructed by integrating the High-Frequency Perception (HFP) and the spatial dependency perception module (SDP), enabling edge-detail enhancement and spatial-semantic alignment during multi-scale fusion. In addition, a lightweight decoupled detection head with SIoU loss is designed to improve the localization of slender components such as waterlines and masts. Experiments on the in-house VitalShips dataset and the public SeaShips dataset show that D-MobileNetV3 improves detection accuracy and generalization relative to the baseline models while maintaining low computational cost. UAV flight tests on the Jetson AGX Xavier platform achieve an average processing time of 16.3 ms and an mAP50 of 83.5%, indicating that the proposed method provides a high-precision, low-latency solution for maritime surveillance.