ERDNet: Efficient Ship Object Detection in Haze Environment.

Abstract

Ship object detection faces the challenge of increasing the difficulty of positioning in hazy environments. Additionally, the latest convolutional neural network (CNN) cannot obtain satisfactory detection results. Therefore, we propose ERDNet, a dual-branch-driven end-to-end network, to improve ship detection accuracy during hazy weather. Specifically, we design a two-branch feature extraction network through complementary attentional fusion to enhance the object feature information of low-quality images. Second, we designed a feature pyramid fusion structure called ERPSA-PAN to aggregate context information effectively. ERPSA-PAN improves the feature fusion capability of the model by suppressing background interference and enhancing useful information. In addition, the spatial-frequency fusion block (SFFB) module with expanded receptive fields is added to the ERDNet detection head to improve the detection ability for multiscale targets. More importantly, we design a robust haze loss to handle different degrees of haze. We introduce two new haze ship datasets, Hazy-SeaShips and Hazy-Boats, which include 17,000 synthetic haze images and 2898 real haze images, respectively, to address the lack of hazy ship image datasets. The images cover variations such as haze thicknesses, ship types, and scales, along with complex backgrounds, and occlusions. The experimental results show that the proposed method is superior to other state-of-the-art (SOTA) methods and achieves relatively competitive results. The source codes, and datasets are available on https://github.com/ZikHH/ERDNet.