Jul 2026· International Conference on Machine Vision and Applications· Vol 14270, pp. 142700S - 142700S-9· 0 citations· 20 references
Engineering
TL;DR
Results indicate that the proposed method effectively improves detection accuracy while maintaining a lightweight model suitable for deployment in complex environments.
Abstract
To address the challenges of object detection in complex weather conditions, including occlusion, blurred boundaries, and small object perception, this paper proposes a lightweight detection model based on YOLOv11n. First, PP-LCNet is adopted as the backbone to reduce the parameter size and computational complexity. Second, a multidimensional collaborative attention module named W-MCA is introduced to enhance channel and spatial feature representation during feature fusion. Third, an adaptive regression loss function called LA-MPDIoU is designed by incorporating center-distance and diagonal-variance terms with learnable weighting to improve localization accuracy. Experimental results on the BDD100K dataset demonstrate that the proposed model achieves a precision of 62.3 percent and a mean average precision of 50.5 percent, showing absolute improvements of several percent over the baseline while using only 2.4 million parameters. These results indicate that the proposed method effectively improves detection accuracy while maintaining a lightweight model suitable for deployment in complex environments.
Object detection in UAV imagery under complex weather conditions remains challenging due to weather-induced image degradation, limited target visibility, and densely distributed small objects. To overcome these limitations, a lightweight detection framework, termed DWP-YOLOv11, is developed based on the YOLOv11 archite...
Liang Li, Zhen-Yan Chu, Wen-Jing Ye et al.· 2026 7th International Confe...· 0 citations
Foggy weather degrades road images by reducing visibility, attenuating object contrast, and blurring boundaries, while autonomous ground vehicles require accurate and lightweight environment perception on resource-constrained onboard platforms. This study proposes Fog-YOLO11n, a lightweight traffic object detector base...
This study tackles issues in autonomous driving by addressing problems with existing detection methods. Traditional single-modal approaches struggle with adaptability, while multimodal shallow fusion algorithms have weak feature coupling. Decision outputs also lack robustness.
This study propose a single-sta...
Xue-Tai Xu, Yang Liu, Hui-Jun Yao· Frontiers of Mechanical Engi...· 0 citations
Detecting small targets in remote sensing imagery remains a critical challenge due to the inherent limitations of low-resolution targets and the frequent clustering of small targets. To address these challenges, we propose an integrated set of improvements to the You Only Look Once (YOLO)v8 framework, leveraging cross-...
Chun-Zhi Li, Sheng-Yan Su, Xiao-Hua Chen et al.· IEEE Transactions on Geoscie...· 0 citations
Detecting small targets such as forest fire points in complex outdoor scenes faces challenges including small target size and strong background interference, and is easily affected by motion blur, illumination changes, and complicated backgrounds. Small targets in remote sensing images typically have low pixel coverage...
The proposed model introduces a lightweight backbone network, an optimized feature pyramid fusion structure, shallow feature enhancement for small objects, attention-based feature refinement, occlusion-aware context enhancement and Soft-NMS post-processing to reduce computational complexity while improving detection st...
