## 1 Introduction
[19], aiming at multi-class marine ship detection, modified the YOLOv5 algorithm by introducing the CSP-DenseNet as a substitute for CSP-DarkNet, leading to higher accuracy when experimenting on a dataset with ships of six classes in the areas of harbors and waterways with heavy traffic. [PERSON] et al. [20] designed a lightweight detection model with modified configurations by applying the MNEBlock, a block constructed based on the integration of the efficient channel attention module into MobileNetV3 in the backbone of Yolov5s, and introducing a coordinate attention mechanism to maintain the detection accuracy. This enabled the detector to perform well on the dataset with complex backgrounds and small ship targets with significantly reduced model size and running memory. In the detection of underwater targets, the main obstacles can be attributed to the poor quality of the input images due to the harsh environments for the recognition tasks, including the effects of blurred targets, weak contrast, low visibility, etc. Much work and effort have been put into modifying the networks and settings for discriminating objects in underwater ecological environments, such as the reconstruction of the network structures with improved attention mechanisms, updated multi-scale feature fusion methods and lightweight modules [21; 22], modification of the confidence loss function [21], the image enhancement preprocessing method [22], etc. In this work, YOLOv5 is selected as the baseline detector for its high detection performance with superior inference speed relative to other state-of-the-art models, which enables the detector to be used in a variety of real-world applications. It can be seen from the aforementioned arguments that deep learning detection models based on YOLOv5 with a high detection accuracy and a high running speed are capable of facilitating real-time target detection applications in marine environments. However, relatively few studies have focused on detecting objects in the sea and river regions, including a combination of vessels, devices, and structures, an important area of application where the performance and generalization abilities of the detector are worth investigating. Remote sensing images (e.g., [18; 20]) have recently become a major source of data for ship recognition tests, while datasets with short-distance views of common maritime objects are scarce. [PERSON], [PERSON], [PERSON], and [PERSON] [19] used the latter type of dataset with multiple classes of ships but focused on the modification of the network structure. Our work differs from these previous studies,in that with the more challenging dataset, we mainly resort to ways of improving the pre-processing of the data and training of the model. It is worthwhile to explore the tricks for the model settings and alterations to the original algorithms in order to raise the inference precision for our specific problem. More specifically, one primary aim of this study is to evaluate the contribution of data augmentation to the performance of the detector by testing different combinations of image transformation methods. Data augmentation is an effective technique for enriching the features of the input data to be learned by the network, especially when the amount of annotated training data is limited, and the choice of and improvements in augmentation methods have the potential to enhance the generalizability of the model to a great extent. However, this is usually not the emphasis of studies on target detection for general purposes or the detection of objects on water surfaces. Another main concern of this work is the study of the form of the loss function since it can significantly affect the speed and convergence of the optimization process [23; 24; 25]. It is beneficial to evaluate the extent to which the selection and design of the loss function affect the performance of the detector and whether a more clever and dynamic formulation can assist in tackling certain challenges resulting from the dataset.