Unmanned Aerial Vehicle (UAV) object detection is crucial in various fields, such as maritime rescue and disaster investigation. However, due to small objects and the limitations of UAVs’ hardware and computing power, detection accuracy and computational overhead are the bottleneck issues of UAV object detection. To address these issues, a novel convolutional neural network (CNN) model, LCSC-UAVNet, is proposed, which substantially enhances the detection accuracy and saves computing resources. To address the issues of low parameter utilization and insufficient detail capture, we designed the Lightweight Shared Difference Convolution Detection Head (LSDCH). It combines shared convolution layers with various differential convolution to enhance the detail capture ability for small objects. Secondly, a lightweight CScConv module was designed and integrated to enhance detection speed while reducing the number of parameters and computational cost. Additionally, a lightweight Contextual Global Module (CGM) was designed to extract global contextual information from the sea surface and features of small objects in maritime environments, thus reducing the false negative rate for small objects. Lastly, we employed the WIoUv2 loss function to address the sample imbalance issue of the datasets, enhancing the detection capability. To evaluate the performance of the proposed algorithm, experiments were performed across three commonly used datasets: SeaDroneSee, AFO, and MOBdrone. Compared with the state-of-the-art algorithms, the proposed model showcases improvements in mAP, recall, efficiency, where the mAP increased by over 10%. Furthermore, it utilizes only 5.6 M parameters and 16.3 G floating-point operations, outperforming state-of-the-art models such as YOLOv10 and RT-DETR.

Berthing is one of the most dangerous phases in the process of ship navigation, and its risk assessment is crucial for both ship safety and port scheduling. To effectively enhance the safety and reliability of the berthing process, a berthing risk assessment method based on the synergy of “human-ship-environment” has been established. First, the impact of the human, ship, and environmental factors on berthing risk was analyzed, and a risk assessment index system for ship berthing was constructed. Then, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were employed for a comprehensive risk assessment. AHP was used to determine the weight of each factor reasonably, while FCE was applied for the evaluation of the berthing risk. Finally, the proposed method was applied to evaluate the berthing operations of two ships, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field, China. The experiment’s results indicate that the evaluation results of the method proposed here have good consistency with the expert survey method.