Addressing degraded weather conditions plays a vital role in practical applications. Many existing restoration approaches are limited to specific weather types, which limits their applicability to different weather scenarios. Advanced technologies, encompassing Transformer and diffusion model, have been harnessed to confront this challenge. However, these methods often heighten network complexity and prolong inference duration. To this end, we present MW-ConvNet, a U-shaped convolution-based network for multi-weather restoration. Specifically, the MW-Enc block and MW-Dec block are introduced to achieve simple yet strong feature extraction, which rely entirely on traditional 2D convolution. To improve adaptability to multiple weather conditions, a prompt generation module is designed to generate a representative weather prompt at the encoder’s terminus. Drawing inspiration from style transfer, the weather prompt is used to guide the decoder learning through a progressive restoration procedure. For future high-fidelity restoration, we introduce frequency separation through wavelet pooling blocks in encoder phase and corresponding up-sampling blocks in decoder phase. The segregated treatment of low-frequency and high-frequency features curbs the loss of textural information during network computation. It also future improves the quality and accuracy of generated weather prompt. Extensive experiments demonstrate that the proposed MW-ConvNet obtains superior performance compared to state-of-the-art methods across both weather-specific and real-world restoration tasks. Significantly, our method achieves an impressive inference speed of 0.12 seconds per 256x256 image, outpacing transformer-based and diffusion-based models.

To enhance ship detection accuracy in the presence of complex scenes and significant variations in object scales, this study introduces three enhancements to ReDet, resulting in a more powerful ship detection model called rotation-equivariant bidirectional feature fusion detector (ReBiDet). Firstly, the feature pyramid network (FPN) structure in ReDet is substituted with a rotation-equivariant bidirectional feature fusion feature pyramid network (ReBiFPN) to effectively capture and enrich multiscale feature information. Secondly, K-means clustering is utilized to group the aspect ratios of ground truth boxes in the dataset and adjust the anchor size settings accordingly. Lastly, the difficult positive reinforcement learning (DPRL) sampler is employed instead of the random sampler to address the scale imbalance issue between objects and backgrounds in the dataset, enabling the model to prioritize challenging positive examples. Through numerous experiments conducted on the HRSC2016 and DOTA remote sensing image datasets, the effectiveness of the proposed improvements in handling complex environments and small object detection tasks is validated. The ReBiDet model demonstrates state-of-the-art performance in remote sensing object detection tasks. Compared to the ReDet model and other advanced models, our ReBiDet achieves mAP improvements of 3.20, 0.42, and 1.16 on HRSC2016, DOTA-v1.0, and DOTA-v1.5, respectively, with only a slight increase of 0.82 million computational parameters.

Camouflaged object detection (COD) is an arduous challenge due to the striking resemblance of camouflaged objects to their surroundings. The abundance of similar background information can significantly impede the efficiency of camouflaged object detection algorithms. Prior research in this domain has often relied on supplementary prior knowledge to guide model training. However, acquiring such prior knowledge is resource-intensive. Furthermore, the additional provided prior information is typically already embedded in the original image, but this information is underutilized. To address these issues, in this paper, we introduce a novel Camouflage Cues Guidance Network (CCGNet) for camouflaged object detection that does not rely on additional prior knowledge. Specifically, we use an adaptive approach to track the learning state of the model with respect to the camouflaged object and dynamically extract the cues of the camouflaged object from the original image. In addition, we introduce a foreground separation module and an edge refinement module to effectively utilize these camouflage cues, assisting the model in fully separating camouflaged objects and enabling precise edge prediction. Extensive experimental results demonstrate that our proposed methods can achieve superior performance compared with state-of-the-art approaches.