专为恶劣天气下儿童行人设计的轻量级热成像检测模型
LTV-YOLO: A Lightweight Thermal Object Detector for Young Pedestrians in Adverse Conditions
- 基于热成像与轻量结构，专精小目标行人检测
- 在复杂环境下实现高精度实时检测，适配边缘设备
- 适合智能交通、自动驾驶及校园安全场景
在低光照和恶劣天气条件下检测易受伤害道路使用者（如儿童和青少年）仍是计算机视觉、监控与自动驾驶系统中的关键挑战。本文提出一种专为热成像优化的轻量级目标检测模型——LTV-YOLO（Lightweight Thermal Vision YOLO），用于识别各类环境下的年轻行人。该模型利用长波红外（LWIR）相机获取的热成像数据，在传统可见光相机失效时仍保持高可靠性。基于YOLO11架构，通过引入深度可分离卷积与特征金字塔网络（FPN），在保持紧凑结构的同时，有效提升对小尺度、部分遮挡及热特征显著的行人目标的检测性能。本工作提供了一种实用且可扩展的解决方案，适用于学校区域、自主导航与智慧城市基础设施中的行人安全提升。区别于以往热成像检测器，LTV-YOLO为仅热成像、面向年幼及远距离行人的专用设计，其在恶劣条件下针对短目标与遮挡行人的优化组合，在当前研究中尚属首创。
原文摘要 · Abstract (English)
Detecting vulnerable road users (VRUs), particularly children and adolescents, in low light and adverse weather conditions remains a critical challenge in computer vision, surveillance, and autonomous vehicle systems. This paper presents a purpose-built lightweight object detection model designed to identify young pedestrians in various environmental scenarios. To address these challenges, our approach leverages thermal imaging from long-wave infrared (LWIR) cameras, which enhances detection reliability in conditions where traditional RGB cameras operating in the visible spectrum fail. Based on the YOLO11 architecture and customized for thermal detection, our model, termed LTV-YOLO (Lightweight Thermal Vision YOLO), is optimized for computational efficiency, accuracy and real-time performance on edge devices. By integrating separable convolutions in depth and a feature pyramid network (FPN), LTV-YOLO achieves strong performance in detecting small-scale, partially occluded, and thermally distinct VRUs while maintaining a compact architecture. This work contributes a practical and scalable solution to improve pedestrian safety in intelligent transportation systems, particularly in school zones, autonomous navigation, and smart city infrastructure. Unlike prior thermal detectors, our contribution is task-specific: a thermally only edge-capable design designed for young and small VRUs (children and distant adults). Although FPN and depthwise separable convolutions are standard components, their integration into a thermal-only pipeline optimized for short/occluded VRUs under adverse conditions is, to the best of our knowledge, novel.
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