Detecting small ship targets via infrared remote sensing

A detection technique called YOLO for weak infrared remote sensing ship target (YOLO-WIT) is presented to overcome the difficulties in detecting weak and small ship targets using infrared remote sensing. Firstly, due to the difficulty of low tolerance against positional offset for dim and small targets, the detection head is optimized to reduce model size while limiting the maximum offset of the anchor. Additionally, a composite distance similarity measure is designed to decrease location sensitivity in the regression branch, thereby enhancing regression accuracy. Secondly, in view of the situation where the infrared image background is bright and the target is dark, a dense concat information expansion convolution block (DECO) is designed to retain weak signals and enhance the perception of weak features. A spatio-temporal attention mechanism is employed for feature enhancement, and the Sobel operator is utilized to solve the first-order derivative of the shallow feature maps in order to direct the model to make judgments with edge features in order to differentiate interference objects with similar shapes and grayscale amplitudes. The experimental results on the NUDT-SIRST-Sea dataset demonstrate that: YOLO-WIT reduces parameters by 31.6% compared to the baseline model, increases mAP50 by 9%, and raises mAP50-95 by 4.9%. In comparison to mainstream detection algorithms, YOLO-WIT demands fewer resources with a model size of just 9.2×106. Its detection performance on dim and small ship targets is notably superior to other methods.