LightSeek-YOLO: A Lightweight Architecture for Real-Time Trapped Victim Detection in Disaster Scenarios
Xiaowen Tian, Yubi Zheng, Liangqing Huang, Rengui Bi, Yu Chen, Shiqi Wang, Wenkang Su · Mathematics · 2025
Rapid and accurate detection of trapped victims is vital in disaster rescue operations, yet most existing object detection methods cannot simultaneously deliver high accuracy and fast inference under resource-constrained conditions. To address this limitation, we propose the LightSeek-YOLO, a lightweight, real-time victim detection framework for disaster scenarios built upon YOLOv11. Our LightSeek-YOLO integrates three core innovations. First, it employs HGNetV2 as the backbone, whose HGStem and HGBlock modules leverage depthwise separable convolutions to markedly reduce computational cost while preserving feature extraction. Secondly, it introduces Seek-DS (Seek-DownSampling), a dual-branch downsampling module that preserves key feature extrema through a MaxPool branch while capturing spatial patterns via a progressive convolution branch, thereby effectively mitigating background interference. Third, it incorporates Seek-DH (Seek Detection Head), a lightweight detection head that processes features through a unified pipeline, enhancing scale adaptability while reducing parameter redundancy. Evaluated on the common C2A disaster dataset, LightSeek-YOLO achieves 0.478 AP@small for small-object detection, demonstrating strong robustness in challenging conditions such as rubble and smoke. Moreover, on the COCO, it reaches 0.473 mAP@[0.5:0.95], matching YOLOv8n while achieving superior computational efficiency through 38.2% parameter reduction and 39.5% FLOP reduction, and achieving 571.72 FPS on desktop hardware, with computational efficiency improvements suggesting potential for edge deployment pending validation.