An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system

Abstract

Abstract The accurate and real-time detection of moving ships has become an essential component in maritime video surveillance, leading to enhanced traffic safety and security. With the rapid development of artificial intelligence, it becomes feasible to develop intelligent techniques to promote ship detection results in maritime applications. In this work, we propose to develop an enhanced convolutional neural network (CNN) to improve ship detection under different weather conditions. To be specific, the learning and representation capacities of our network are promoted by redesigning the sizes of anchor boxes, predicting the localization uncertainties of bounding boxes, introducing the soft non-maximum suppression, and reconstructing a mixed loss function. In addition, a flexible data augmentation strategy with generating synthetically-degraded images is presented to enlarge the volume and diversity of original dataset to train learning-based ship detection methods. This strategy is capable of making our CNN-based detection results more reliable and robust under adverse weather conditions, e.g., rain, haze, and low illumination. Experimental results under different monitoring conditions demonstrate that our method significantly outperforms other competing methods (e.g., SSD, Faster R-CNN, YOLOv2 and YOLOv3) in terms of detection accuracy, robustness and efficiency. The ship detection results under poor imaging conditions have also been implemented to demonstrate the superior performance of our learning method.