LTV-YOLO: A Lightweight Thermal Object Detector for Young Pedestrians in Adverse Conditions

2026-01-15

AI Summary

This work addresses the significant decline in detection reliability of vulnerable road users—particularly children—under low-light and adverse weather conditions. To this end, the authors propose a lightweight thermal imaging–based object detection model built upon the YOLOv11 architecture, which, for the first time, integrates depthwise separable convolutions with a Feature Pyramid Network (FPN) within a pure long-wave infrared (LWIR) pipeline. The model is specifically optimized for detecting small-scale and partially occluded child pedestrians. It achieves high-accuracy, real-time performance on edge devices and demonstrates robust detection capabilities across diverse challenging environments for both children and distant adult pedestrians. The design balances model compactness and deployment efficiency, making it well-suited for applications in intelligent transportation systems and school-zone safety monitoring.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• vulnerable road users
• thermal object detection
• adverse weather conditions
• low-light detection
• young pedestrians

Innovation

• LTV-YOLO
• thermal object detection
• lightweight model
• depthwise separable convolution
• feature pyramid network