Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation

Merchant ships sometimes fail to detect small ships at night and in poor visibility, leading to urgent situations and even collisions. Infrared (IR) cameras have inherent advantages in small target detection and become essential environmental awareness equipment on unmanned ships. The existing target detection models are complex and difficult to deploy on small devices. Lightweight detection algorithms are needed with the increase in the number of shipborne cameras. Therefore, herein, a lightweight model for small IR ship detection was selected as the research object. IR videos were collected in the Bohai Strait, the image sampling interval was calculated, and an IR dataset of small ships was constructed. Based on the analysis of the characteristics of the IR ship images, gamma transform was used to preprocess the images, which increased the gray difference between the target and background. The backbone of YOLOv5 was replaced with that of Mobilev3 to improve the computing efficiency. Finally, the results showed that the parameters of the proposed model were reduced by 83% compared with those of the YOLOv5m model, while the detection performance was almost the same.