Ship recognition is a technology that analyzes image features, such as color, shape, and texture, and has numerous applications in the field of intelligent transportation [1–3]. Due to the development of inland waterway transport modes and the increasing density of inland waterway traffic, automatic ship identification and tracking systems face significant challenges [4,5]. Traditional ship identification methods are no longer sufficient to cope with the complexity of the traffic environment. For instance, these methods cannot identify vessels without information on sailing time, direction, or speed. Additionally, existing vessel identification methods require extensive preprocessing of image data, leading to large data volumes and storage space requirements.

The most commonly used ship recognition technologies in the field of intelligent transportation include Automatic Identification System (AIS) based on computer vision [6,7], Global Positioning System (GPS) based on Light Detection and Ranging (LiDAR) [8,9], and Human-Aided Inertial System (HAIS) based on Electronic Chart Display and Information System (ECDIS) [10,11]. Although these ship recognition methods based on communication and navigation equipment have their advantages, they also have significant limitations in traffic-intensive waters such as ports because they cannot obtain visual images of ships. Currently, research on deep learning-based ship recognition methods has made progress through automatic extraction of ship image features and continuous learning and training, achieving ship recognition in maritime traffic videos and images [12–15]. Deep learning-based ship recognition methods mainly adopt two approaches: introducing deep learning algorithms into ship recognition and fully utilizing image information through image pre-processing. Recently, deep learning-based object detection methods, such as You Only Look Once (YOLO), have achieved remarkable success in ship detection and recognition. However, YOLOv7 models have limitations in adapting to diverse ship shapes and sizes. Therefore, in this paper, we propose an improved YOLOv7 model that addresses these challenges and achieves better performance in ship detection and recognition.

This paper makes significant contributions in several areas. First, the YOLOv7 model structure is improved by replacing anchor boxes with more suitable ones for ship detection and adding a multi-scale feature fusion module to better capture ship features of different scales. Second, data preprocessing techniques such as data augmentation and sample balancing are adopted to increase data diversity, robustness, training efficiency, and model generalization. Third, extensive experiments on benchmark datasets demonstrate the proposed method’s effectiveness in achieving state-of-the-art performance in terms of both detection accuracy and efficiency. Overall, the paper provides a valuable contribution to the field of ship detection and recognition.

The paper is structured as follows: Section 2 reviews related works on target detection. Section 3 explains the improvements made to the YOLOv7 model. Section 4 outlines the experiment methodology. Section 5 presents the results and discussion. Finally, Section 6 provides a summary of the paper.

Target detection algorithms represented by deep learning can be divided into two main categories: two-stage target detection algorithms and one-stage target detection algorithms. Implementing two-stage target detection involves two processes, extracting the object region and then performing CNN classification on the region to identify it. One-stage target detection requires only one extraction of features to achieve target detection. In contrast, one-stage target detection allows for fast detection requirements but may be slightly less accurate than dual-stage target detection.

To evaluate the ship recognition performance of the improved YOLOv7 algorithm, this experiment was trained and tested on the SeaShips dataset.

Introduction to the dataset. The Seaships dataset [35] was collected to train and evaluate the capability of the target detection algorithm in ship detection. 7000 1920 × 1080 images were collected in SeaShips. Each image in the SeaShips dataset is annotated with the exact ship label and bounding box, as shown in Fig. 2. The SeaShips dataset was created from images taken by an on-site video surveillance system deployed around Hengqin Island, Zhuhai, China. The images selected for the dataset images cover different features including different ship types, hull sections, scale, viewing angles, lighting and different levels of occlusion in various complex environments. Different degrees of obscuration in complex environments.

Experimental equipment. Parameters of the model training device used in the experiments: OS, Windows 10; GPU, NVIDIA GeForce RTX 3080 Ti; CPU, 8× Xeon E5-2686 v4; deep learning framework, Torch 1.9.0+CUDA 11.1.

Experimental parameter settings. Due to the limitations of the experimental equipment, we scaled the length and width of the input image to 1/2 of the original size, i.e., 960 × 540 pixels. The optimizer used SGD, the learning rate was set to 0.01, the momentum was 0.9, the weight decay was 0.0005, the batch size was 16, and 200 epochs were trained, with 10 training epochs and 1 test epoch alternating.

Assessment methods. Evaluation metrics are essential for assessing the performance of a model. In this paper, Precision, Recall, and mAP (mean average precision of the entire class) have been chosen as evaluation metrics. All three metrics have a range of values from 0 to 1, where the closer the value is to 1, the better the detection accuracy and model performance. The experiments employed mAP@0.5 (mean Average Precision, with IoU threshold greater than 0.5) as the evaluation metric to compute the mean average precision for all classes. We chose to use precision instead of accuracy because, in many cases, precision is a more suitable metric for evaluating machine learning models, particularly when working with imbalanced datasets where the number of positive and negative samples is unequal. Accuracy can be deceptive in such cases, as it may appear high solely due to the abundance of negative samples, while the model’s ability to correctly identify positive samples may be poor. (1)Precision=TPTP+FP (2)Recall=TPTP+FN

In the above equation, TP indicates the number of predictions that agree with the true class; FP indicates the number of samples where the true class is negative but the prediction is positive; and FN indicates the number of samples where the true class is positive but the prediction is negative. The full class average precision is obtained by taking a weighted average of the average correctness of all class tests, and its value can also be expressed as the area enclosed by Precision and Recall as the two axes of a right-angle coordinate system respectively.

During the training phase of the model, the changes in Recall can be observed from Fig. 3a, while the changes in Precision can be seen in Fig. 3b, and the changes in mAP can be derived from Fig. 3c. After 100 epochs of training, the magnitudes of changes in Precision and Recall gradually decreased. However, due to certain labeling errors and uneven data distribution in the experimental dataset, Precision and Recall fluctuated greatly during the training process. The changes in mAP gradually decreased after about 50 iterations of the model. After 200 iterations of the improved YOLOv7 algorithm, the final mAP of the model was maintained at 90.15%.

Fig. 4 presents the precision-recall (P-R) curve plots for each model in the field of ship identification. It is evident that the area enclosed by the P-R curve formed by the improved YOLOv7 model is significantly larger than the other two models. It is well-known that the area enclosed by the P-R curve and the coordinate axis is the mAP value, and a higher mAP value indicates better detection performance of the model. Therefore, the results demonstrate that the improved YOLOv7 outperforms the other models in the field of ship identification detection.

Fig. 5 displays the category accuracy and mAP values of the three models for six different categories of boats. As shown in the figure, the detection accuracy of the improved YOLOv7 algorithm reaches 90.15%, with a particularly high mAP for small fishing boats at 91.89%, which is significantly better than the SSD and YOLOv7 models. This result highlights the advantages of the improved YOLOv7 model in inland waterway ship identification detection, which can meet the detection needs for intelligent ship navigation.

Fig. 6 showcases some of the ship detection results obtained by the improved YOLOv7 algorithm. The results demonstrate that the algorithm is capable of accurately detecting different types of ships, regardless of their size. In Figs. 6b and 6d, we can see that the algorithm can identify both large and small ships with high confidence. Moreover, the improved YOLOv7 algorithm is capable of detecting multiple targets in a single image and accurately identifying each target category separately, as demonstrated in Fig. 6f. These results showcase the effectiveness and robustness of the proposed algorithm in detecting ships in complex environments.

In summary, the proposed improved YOLOv7 model with specifically designed anchor boxes, a novel multi-scale feature fusion module, and enhanced data preprocessing with data augmentation and balanced sampling, has shown significant improvements in ship detection and recognition tasks. The experimental results demonstrate that the improved YOLOv7 model outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness, with a final mAP of 90.15%. The model has demonstrated high accuracy and mAP values for small fishing boats, making it a suitable option for inland waterway ship identification detection. Additionally, the algorithm can accurately detect different types of ships of varying sizes, even in complex environments, and can identify multiple targets in a single image with high confidence. These findings highlight the potential of the improved YOLOv7 model for practical applications in the maritime domain.