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.

Video stabilization aims to eliminate random jitter in video sequences, but most methods result in stabilized video with degraded resolution and content loss. In this letter, we propose a full-frame video stabilization algorithm based on motion intent analysis. The algorithm consists of three main steps: motion estimation, motion smoothing, and video completion. First, robust keypoints are extracted using the improved SuperPoint network and refined with the suppression via square covering (SSC) algorithm to obtain stable and reliable keypoints. Then, the Lucas-Kanade algorithm is applied for motion estimation of inter-frame matched feature points. Second, motion smoothing is achieved using the Kalman filtering algorithm to remove the high-frequency jitter component from the trajectory, and motion compensation is applied to the original video sequence to generate a stable image sequence. Finally, to preserve the original video resolution, we propose a video completion method based on motion intent analysis. Experimental results demonstrate that our method achieves higher stability while maintaining the original video resolution compared to the current state-of-the-art video stabilization algorithms.