Maritime search and rescue is a crucial component of the national emergency response system, which mainly relies on unmanned aerial vehicles (UAVs) to detect objects. Most traditional object detection methods focus on boosting the detection accuracy while neglecting the detection speed of the heavy model. However, improving the detection speed is essential, which can provide timely maritime search and rescue. To address the issues, we propose a lightweight object detector named Shuffle-GhostNet-based detector (SG-Det). First, we construct a lightweight backbone named Shuffle-GhostNet, which enhances the information flow between channel groups by redesigning the correlation group convolution and introducing the channel shuffle operation. Second, we propose an improved feature pyramid model, namely BiFPN-tiny, which has a lighter structure capable of reinforcing small object features. Furthermore, we incorporate the Atrous Spatial Pyramid Pooling module (ASPP) into the network, which employs atrous convolution with different sampling rates to obtain multi-scale information. Finally, we generate three sets of bounding boxes at different scales—large, medium, and small—to detect objects of different sizes. Compared with other lightweight detectors, SG-Det achieves better tradeoffs across performance metrics and enables real-time detection with an accuracy rate of over 90% for maritime objects, showing that it can better meet the actual requirements of maritime search and rescue.

Unmanned Aerial Vehicles (UAVs) play an extremely important role in real-time object detection for maritime emergency rescue missions. However, marine accidents often occur in low-visibility weather conditions, resulting in poor image quality and a lack of object detection samples, which significantly reduces detection accuracy. To tackle these issues, we propose GD-Det, a low-data object detection model with high accuracy, specifically designed to handle limited sample sizes and low-quality images. The model is primarily composed of three components: (i) A lightweight re-parameterization feature extraction module which integrates RepVGG blocks into multi-concat blocks to enhance the model’s spatial perception and feature diversity during training. Meanwhile, it reduces computational cost in the inference phase through the re-parameterization mechanism. (ii) A cross-scale gather-and-distribute pyramid module, which helps to augment the relationship representation of four-scale features via flexible skip fusion and distribution strategies. (iii) A decoupled prediction module with three branches is to implement classification and regression, enhancing detection accuracy by combining the prediction values from tri-level features. (iv) We also use a domain-adaptive training strategy with knowledge transfer to handle low-data issues. We conducted low-data training and comparison experiments using our constructed dataset AFO-fog. Our model achieved an overall detection accuracy of 84.8%, which is superior to other models.