Improved Ship Object Detection in Low-Illumination Environments Using RetinaMFANet

Abstract

Video-based ship object detection has long been a popular research issue that has received attention in the water transportation industry. However, in low-illumination environments, such as at night or in fog, the water environment has a complex variety of light sources, video surveillance images are often accompanied by noise, and information on the details of objects in images is worsened. These problems cause high rates of false detection and missed detection when performing object detection for ships in low-illumination environments. Thus, this paper takes the detection of ship objects in low-illumination environments at night as the research object. The technical difficulties faced by object detection algorithms in low-illumination environments are analyzed, and a dataset of ship images is constructed by collecting images of ships (in the Nanjing section of Yangtze River in China) in low-illumination environments. In view of the outstanding performance of the RetinaNet model in general object detection, a new multiscale feature fusion network structure for a feature extraction module is proposed based on the same network architecture, in such a way that the extraction of more potential feature information from low-illumination images can be realized. In line with the feature detection network, the regression and classification detection network for anchor boxes is improved by means of the attention mechanism, guiding the network structure in the detection of object features. Moreover, the design and optimization of the augmentation of multiple random images and prior bounding boxes in the training process are also carried out. Finally, on the basis of experimental validation analysis, the optimized detection model was able to improve ship detection accuracy by 3.7% with a limited decrease in FPS (frames per second), and has better results in application.