The targets in Synthetic Aperture Radar (SAR) images are often tiny, irregular, and difficult to detect against complex backgrounds, leading to a high probability of missed or incorrect detections by object detection algorithms. To address this issue and improve the recall rate, we introduce an improved version of YOLOv8 (You Only Look Once), named MDD-YOLOv8. This model is not only fast but also highly accurate, with fewer instances of missed or incorrect detections. Our proposed model outperforms the baseline YOLOv8 in SAR image detection by utilizing dynamic convolution to replace static convolution (DynamicConv) and incorporating a deformable large kernel attention mechanism (DLKA). Additionally, we modify the structure of the FPN-PAN and introduce an extra detection header to better detect tiny objects. Experiments on the MSAR-1.0 dataset demonstrate that MDD-YOLOv8 achieves 87.7% precision, 76.1% recall, 78.9% mAP@50, and 0.81 F1 score. These metrics show an improvement of 8.1%, 6.0%, 6.9%, and 0.07, respectively, compared to the original YOLOv8. Although, MDD-YOLOv8 increases parameters by about 20% and GFLOPs by 53% more than YOLOv8n. To further validate the model’s effectiveness, we conducted generalization experiments on four additional SAR image datasets, proving that MDD-YOLOv8’s performance is robust and universally applicable. In summary, MDD-YOLOv8 is a robust, generalized model with strong potential for industrial applications.

Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions and recover fine details. To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. To advance the research in nighttime glare suppression, we constructed and publicly released the Night Road Glare Dataset (NRGD) captured in real nighttime road scenarios, enriching the evaluation system for this task. Experiments conducted on the Flare7K++ and NRGD, using five evaluation metrics and comparing six state-of-the-art methods, demonstrate that our method achieves superior performance in both subjective and objective metrics compared to existing advanced methods.

Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with large parameters, high computational complexity and difficulty in deploying on edge devices, this paper proposes a lightweight glare suppression network (LGSNet) based on ghost depthwise separable convolution and Lightweight Parallel Attention. Based on the U-Net architecture, the network introduces ghost depthwise separable convolution blocks (GhostDSC) in the encoder and decoder, which generates ghost features through cheap linear transformations by exploiting feature map redundancy, significantly reducing model parameters and computational costs while maintaining feature representation ability. Meanwhile, a Lightweight Parallel Attention (LPA) module is designed in the decoder stage, which integrates channel attention and pixel attention in parallel, enhancing the network’s attention to glare regions and edge details with extremely low parameter increment to improve detail recovery accuracy. In addition, a joint loss function consisting of background loss, glare loss and reconstruction loss is constructed to collaboratively optimize glare suppression and detail preservation. Experimental results on the public Flare7K++ dataset and the self-built nighttime road glare dataset NRGD show that the proposed method has only 7.45 M parameters, much lower than standard U-Net and Uformer. It achieves competitive results on full-reference metrics such as PSNR, SSIM, LPIPS and no-reference metrics such as NIQE, BRISQUE, PIQE, and can effectively suppress various types of glare interference and restore obscured scene details. It achieves a superior trade-off between model complexity and enhancement performance, significantly reducing the parameter count and computational overhead compared to heavy baselines, thereby offering a highly efficient solution for resource-aware glare suppression tasks.