Year: 2025
Type: article
Abstract: Rockfalls on mountainous roads pose significant safety risks to pedestrians and vehicles, particularly in remote areas with underdeveloped communication infrastructure. To enable efficient detection, this study proposes a rockfall detection system based on embedded technology and an improved Yolov8 algorithm, termed Yolov8-GCB. The algorithm enhances detection performance through the following optimizations: (1) integrating a lightweight DeepLabv3+ road segmentation module at the input stage to generate mask images, which effectively exclude non-road regions from interference; (2) replacing Conv convolution units in the backbone network with Ghost convolution units, significantly reducing model parameters and computational cost while improving inference speed; (3) introducing the CPCA (Channel Priori Convolution Attention) mechanism to strengthen the feature extraction capability for targets with diverse shapes; and (4) incorporating skip connections and weighted fusion in the Neck feature extraction network to enhance multi-scale object detection. Experimental results demonstrate that Yolov8-GCB improves [email protected] and [email protected] by 1.2% and 1%, respectively, while reducing the number of parameters by 14.1% and the GFLOPs by 16.1% and increasing inference speed by 20.65%. This method provides an effective technological solution for real-time rockfall detection on embedded devices and can be extended to other disaster scenarios, such as landslides and debris flows, in regions with limited infrastructure.
Source: Scientific Reports
Authors: Peng Peng, Langchao Gao, Jiachun Li, Hongzhen Zhang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G3227551177
OpenAlex ID: W4409714748