Abstract
The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.
Highlights
Ship detection is of great significance for marine automatic fishery management, port rescue, marine traffic maintenance, and other applications [1,2,3,4]
Attention mechanism [10] introduces the spatial or channel significance to the feature maps extracted by the convolutional neural network (CNN), which contributes by focusing on the characteristic properties of interested targets
Speaking, for a heavy sea clutter scene, as shown in the first column of Figure 5, one can see that the Attention Mask Region-based Convolutional Neural Network (R-CNN) misses the only small ship, whereas the Faster R-CNN and DC-SPP-you only look once (YOLO) methods show a different amount of false detection
Summary
Ship detection is of great significance for marine automatic fishery management, port rescue, marine traffic maintenance, and other applications [1,2,3,4]. The YOLO-based networks, as typical end-to-end and concise pipelines, treat object detection as a regression problem without the region generation step [8], achieving excellent computational efficiency in many challenging benchmark datasets. These models find it difficult to distinguish the foreground from the background in a complex ocean scene [9], especially for detecting small-scale ships, resulting in false and missed alarms. 2021, 13, 3059 a novel small ship detection method termed as portable attention-guided YOLO (PAGYOLO), which can improve the detection performance without additional computation cost.
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