Real-time detection of apple leaf diseases in orchard environments faces ongoing challenges, particularly in preserving fine-grained disease features with limited computing resources. To address these issues, we propose a high-precision lightweight model based on YOLOv10n, called YOLO-ALD. First, we introduce Spatial and Channel Reconstruction Convolution into deeper backbone networks to replace standard downsampling layers and convolutions. This suppresses spatial and channel redundancy caused by environmental noise and optimizes feature representation. Second, we design a new C2f-Faster-SimAM module for the neck network. This module combines the inference efficiency of FasterNet with a parameter-free 3D attention mechanism to adaptively focus on early lesions, effectively distinguishing them from leaf veins without increasing model complexity. Third, in the detection head section, we use the Focaler-ShapeIoU loss function to optimize bounding box regression. It utilizes a dynamic focusing mechanism and geometric constraints to ensure the localization accuracy of irregular shapes and hard-to-detect samples. Experimental results on our self-built dataset covering four specific diseases and healthy leaves showed that, compared with YOLOv10n, the mAP@0.5 of YOLO-ALD reached 92.1%, achieving a 2.1% increase. In addition, the model has an inference speed of 105 FPS, with only 2.1 M parameters and 5.6 GFLOPs. Therefore, YOLO-ALD achieves a good balance between efficiency and robustness, showing strong theoretical potential for resource-constrained mobile agriculture diagnosis.

Face recognition technology has been widely used in all aspects of people's lives. However, the accuracy of face recognition is greatly reduced due to the obscuring of objects, such as masks and sunglasses. Wearing masks in public has been a crucial approach to preventing illness, especially since the Covid-19 outbreak. This poses challenges to applications such as face recognition. Therefore, the removal of masks via image inpainting has become a hot topic in the field of computer vision. Deep learning-based image inpainting techniques have taken observable results, but the restored images still have problems such as blurring and inconsistency. To address such problems, this paper proposes an improved inpainting model based on generative adversarial network: the model adds attention mechanisms to the sampling module based on pix2pix network; the residual module is improved by adding convolutional branches. The improved inpainting model can not only effectively restore faces obscured by face masks, but also realize the inpainting of randomly obscured images of human faces. To further validate the generality of the inpainting model, tests are conducted on the datasets of CelebA, Paris Street and Place2, and the experimental results show that both SSIM and PSNR have improved significantly.

Image super-resolution (SR) models based on the generative adversarial network (GAN) face challenges such as unnatural facial detail restoration and local blurring. This paper proposes an improved GAN-based model to address these issues. First, a Multi-scale Hybrid Attention Residual Block (MHARB) is designed, which dynamically enhances feature representation in critical face regions through dual-branch convolution and channel-spatial attention. Second, an Edge-guided Enhancement Block (EEB) is introduced, generating adaptive detail residuals by combining edge masks and channel attention to accurately recover high-frequency textures. Furthermore, a multi-scale discriminator with a weighted sub-discriminator loss is developed to balance global structural and local detail generation quality. Additionally, a phase-wise training strategy with dynamic adjustment of learning rate (Lr) and loss function weights is implemented to improve the realism of super-resolved face images. Experiments on the CelebA-HQ dataset demonstrate that the proposed model achieves a PSNR of 23.35 dB, a SSIM of 0.7424, and a LPIPS of 24.86, outperforming classical models and delivering superior visual quality in high-frequency regions. Notably, this model also surpasses the SwinIR model (PSNR: 23.28 dB → 23.35 dB, SSIM: 0.7340 → 0.7424, and LPIPS: 30.48 → 24.86), validating the effectiveness of the improved model and the training strategy in preserving facial details.

To address difficulties in traffic sign detection under low-light environments, this paper proposes RiDW-YOLO, an improved detection algorithm based on YOLOv11n. The Retinexformer network is embedded as a trainable front-end module at the first layer of the YOLOv11n backbone, performing online image enhancement during forward propagation without offline preprocessing. Its weights are updated end-to-end with the subsequent detection sub-network, improving brightness and contrast while suppressing noise, thereby strengthening feature extraction for traffic sign targets. An iterative attentional feature fusion (iAFF) block is integrated into the feature-fusion architecture of YOLOv11n, enabling adaptive weighted multi-level feature aggregation and enhancing feature representation. DySample, a dynamic up-sampling operator, replaces conventional interpolation methods by learning offset coordinates to better preserve fine-grained feature information. Wise-IoU (WIoU) replaces the original Complete-Intersection over Union (CIoU) loss function, leveraging dynamically adjusted gradient weights to suppress low-quality samples interference and boost bounding-box localization performance. Experimental results demonstrate that compared with the YOLOv11n baseline model, the improved algorithm achieves an increase of 15.2 percentage points in precision, 6.2 percentage points in recall, and 11.0 percentage points in mAP@50. Overall, this work provides a feasible solution for traffic-sign detection under low-light conditions and emphasizes the importance of balanced module design and frank discussion of current limitations.

Detecting traffic signs in real-world roadway scenes remains a demanding task due to extensive category diversity, the prevalence of diminutive targets, and interference from cluttered surroundings. To overcome these obstacles, we present YOLO-PPA, a YOLOv11n-based detector strengthened through multi-path feature aggregation and attention-enhanced representation learning. First, a Parallelized Patch-Aware Attention (PPA) mechanism is embedded in place of the standard C3K2 block, simultaneously capturing fine-grained local textures and broad contextual semantics while adaptively amplifying informative spatial regions critical for small objects. Second, a high-resolution P2 detection head is appended to the feature pyramid, recovering fine spatial cues that would otherwise be attenuated across successive downsampling stages, and this design is particularly beneficial for recognizing signage occupying only a handful of pixels. Third, the Normalized Gaussian Wasserstein Distance (NWD) replaces the conventional CIoU metric as the regression loss, offering a smoother optimization landscape for tiny instances where even single-pixel displacements can destabilize standard IoU-based objectives. Evaluated on the TT100K benchmark, YOLO-PPA surpasses the YOLOv11n baseline by 2.1% in precision, 3.7% in recall, 4.3% in mAP@50, and 3.0% in mAP@50:95, confirming its effectiveness for small-scale traffic sign recognition in complex driving environments.