An improved maritime object detection algorithm, SRC-YOLO, based on the YOLOv4-tiny, is proposed in the foggy environment to address the issues of false detection, missed detection, and low detection accuracy in complicated situations. To confirm the model’s validity, an ocean dataset containing various concentrations of haze, target angles, and sizes was produced for the research. Firstly, the Single Scale Retinex (SSR) algorithm was applied to preprocess the dataset to reduce the interference of the complex scenes on the ocean. Secondly, in order to increase the model’s receptive field, we employed a modified Receptive Field Block (RFB) module in place of the standard convolution in the Neck part of the model. Finally, the Convolutional Block Attention Module (CBAM), which integrates channel and spatial information, was introduced to raise detection performance by expanding the network model’s attention to the context information in the feature map and the object location points. The experimental results demonstrate that the improved SRC-YOLO model effectively detects marine targets in foggy scenes by increasing the mean Average Precision (mAP) of detection results from 79.56% to 86.15%.

To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight network and attention mechanism was proposed. Based on the YOLOv4 algorithm, the backbone network CSPDarknet53 was replaced with a lightweight network MobilenetV1 to reduce the model's size. An attention mechanism was added to the three channels before the output to increase its ability to extract forest fire smoke effectively. The algorithm used the K-means clustering algorithm to cluster the smoke dataset, and obtained candidate frames that were close to the smoke images; the dataset was expanded to 2000 images by the random flip expansion method to avoid overfitting in training. The experimental results show that the improved YOLOv4 algorithm has excellent detection effect. Its mAP can reach 93.45%, precision can get 93.28%, and the model size is only 45.58 MB. Compared with YOLOv4 algorithm, MoAm-YOLOv4 improves the accuracy by 1.3% and reduces the model size by 80% while sacrificing only 0.27% mAP, showing reasonable practicability.

Siamese networks have gained considerable attention for object tracking due to their balance of speed and accuracy. However, existing Siamese tracking algorithms have been too rigid in their predictions of bounding box tags and lack uncertainty estimation, resulting in poor tracking performance in marine environments, particularly those with waves. To improve the effectiveness of trackers in marine environments, this study proposes a Siamese distillation network. First, to address the issue that the presence of waves and other disturbances may result in target loss or inaccuracy when tracking the target, the concept of a probability distribution of the bounding box is introduced in this study, which transforms the standard Dirac delta distribution of the bounding box into a probability distribution of the bounding box, effectively reducing the impact of interference on tracking performance and improving target location accuracy. Second, we chose ResNet100 as the backbone network to obtain richer features for localization. Finally, this work offers a knowledge distillation approach to further enhance the tracking accuracy and model performance, while considering the impact of the model's number of parameters and computational amount on tracking performance. This network outperforms most trackers in terms of accuracy, according to extensive experimental results, and performs well on the target tracking benchmark and marine dataset annotated in this study. Specifically, this network achieved the highest accuracy value of 0.612 compared to other Siamese networks, resulting in a 2.5% increase compared to original baseline network. This suggests that the proposed algorithm is practical.INDEX TERMS Bounding box probability distribution, knowledge distillation, object tracking, siamese network.