## 1 Introduction  
With the rapid development of marine technology in recent years, the detection of objects in sea and ocean environments has become an increasingly crucial area of research and application. Intelligent systems that can recognize targets with little use of manpower have been playing a vital role in harbor management, navigation, ship rescue, collision prevention, and so on [1]. Situations with targets of a variety of categories and sizes under different weather and optical conditions must be dealt with, which places high requirements on the system in recognition and decision-making at a sufficient precision under different circumstances. However, the scarcity of maritime target datasets hinders the progress of study in this area. Object detection algorithms in computer vision that can be employed in marine applications can be divided into traditional methods and deep-learning-based ones, with the latter being widely implemented and improved recently for detection and recognition tasks [2; 3; 4]. One of the typical two-stage detection algorithms is R-CNN [5], which tackled the problems of classification and localization in two steps, i.e., generating region proposals that potentially contain objects with the selective search algorithm and then performing feature extraction using the convolutional neural networks. This algorithm was further modified and enhanced with variants such as Fast R-CNN [6] and Faster R-CNN [7] that surpassed the original algorithm in model performance. Other two-stage detectors, such as R-FCN [8], Cascade R-CNN [9], and Mask R-CNN [10] extended on the basis of R-CNN or its variant, can also gain remarkable performance in object detection tasks. Single-stage algorithms such as the You-Only-Lock-Once (YOLO) series [11; 12], single-shot multi-box detector (SSD) [13], and RetinaNet [14] can usually run at much higher speeds at inference. YOLOv1 [11] provided unified training for classification and localization, reaching an inference speed of 45 fps (much faster than Faster R-CNN) and a mean average precision (mAP) score of 63.4% (comparable to Faster R-CNN (ZF version)) evaluated on the PASCAL VOC [15] 2007 and 2012 datasets. Later, modifications were made to this original algorithm that led to the generation of several follow-up versions (YOLOv2 to YOLOv5). YOLOv5 was released shortly after YOLOv4, with model sizes that were significantly reduced and a faster training speed compared with the latter. Models with different scales of the network can be selected, including YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. These models share the same type of network structure but vary in width and depth. Larger networks are capable of containing models with more complexity at the cost of an increase in training and inference time. Known for its fast processing speed and high accuracy, the YOLOv5 model [12] has been employed extensively in a wide range of areas and real-world applications in recent years. For instance, [PERSON] et al. [16] constructed a remote sensing super-resolution object detection dataset that provided public access to data for various classes with small targets and proposed the multi-class cyclic generative adversarial network with residual feature aggregation with YOLOv5 detector, which significantly enhanced the detection performance of small objects. In the work of [PERSON], [PERSON], [PERSON], and [PERSON] [3], a revised algorithm based on the YOLOv5 model, YOLO-Z, was proposed with a special focus on the improvements in the detection of small targets for autonomous vehicle systems. Upgrades to the original model include replacing the backbone with DenseNet, using biFPN instead of FPN [17] (feature pyramid network) (for YOLO-Z X), and utilizing higher-resolution feature maps. In the area of ship detection, [PERSON] et al. [18] carried out recognition tasks using a modified YOLOv5 with an improved K-means algorithm, the distance-IOU (DIOU) loss, and the channel attention mechanism to tackle the problem of small dense objects. [PERSON] et al.