Identifying Ships From Radar Blips Like Humans Using a Customized Neural Network

An experienced helmsman can always distinguish ships from a pile of radar blips in scenarios such as nearshore waters and inland rivers with a single glance. To replicate this intelligence, a novel approach called MRNet based on deep convolutional networks is proposed. It employs a highly customized neural network to extract critical information from successive radar scans, ranging from low-level characteristics to high-level semantics. The feature fusion network of MRNet is also built with a Depthwise Separable Convolution-based network, which reduces parameter size and calculational usage while improving overfitting issues significantly. In the final prediction procedure, a method based on weighted-box fusion and a Scylla-IoU function is used to accelerate convergence. A marine radar image dataset, namely radar3000, was established to validate the proposed approach. In the corresponding experiments, the recall, identification accuracy, and precision of MRNet reached 0.9663, 0.9418, and 0.9267 respectively. On the other hand, the parameter size and calculational consumption were controlled to only 34.41M and 21.55G respectively. Compared with the commonly-used fractal algorithms and the YOLO series, the MRNet can be described as significantly superior in the application of recognizing ships from marine radar blips, especially in crowded scenarios, which is very similar to human eyes, and can be of great use to navigation and coastal surveillance.