With the rapid development of intelligent manufacturing and smart logistics, object detection has become increasingly important in automated transportation systems, including automated guided vehicles (AGVs), warehouses, production workshops, and distribution operations. However, under adverse weather conditions, existing object detection methods often suffer from degraded performance because object features become blurred or less distinguishable, resulting in reduced detection accuracy. To address this issue, this study proposes an improved object detection algorithm for adverse weather conditions based on YOLOv8n. Specifically, the SimAM attention mechanism is introduced into the backbone network to enhance feature representation. An LCAHead detection head is designed to improve cross-layer feature fusion. In addition, the Wise-IoUv1 loss function is used to replace CIoU, contributing to more stable training and improved convergence. Finally, channel-wise distillation is applied to further enhance detection accuracy without increasing inference cost. Experimental results on the test set show that the proposed method achieves an mAP@0.5 of 50.8%, representing a 7.6% improvement over YOLOv8n, while maintaining an inference speed of 128 FPS.

With the development of intelligent manufacturing and smart logistics, automated guided vehicles (AGVs) are gradually expanding from traditional indoor environments to factory roads, logistics parks, industrial parks, and other outdoor road environments. However, low-visibility conditions, such as fog, rain, snow, and sandstorms, can degrade image quality, weaken object boundaries, and reduce the accuracy of visual perception systems. To address these challenges, an improved lightweight object detection model based on YOLOv11, named DMSM-YOLO, is proposed. To handle blurred boundaries, weak target features, and background interference in low-visibility images, the model is optimized from feature extraction, feature fusion, and prediction refinement. First, EPConv is designed to enhance directional contour interaction and improve blurred-boundary representation. Second, C3k2-MDFI is constructed to strengthen multi-scale weak-target representation. Third, MLCA is introduced to recalibrate low-contrast fused features by combining local spatial details and global contextual information. Finally, SMDetect is developed to improve localization accuracy and confidence estimation for blurred and occluded objects. Experimental results show that the proposed model achieves mAP@0.5 values of 54.71% and 71.67% on the DAWN and RTTS datasets, respectively. Compared with YOLOv11n, the mAP@0.5 is improved by 5.36 and 3.26 percentage points, respectively, while the model maintains a compact size of 2.56 M parameters and 7.1 GFLOPs. Additional experiments further verify the effectiveness of the proposed method under low-light conditions. The proposed model improves detection accuracy and robustness under low-visibility conditions while maintaining lightweight characteristics, demonstrating its potential for AGV visual perception.