A Lightweight Real-Time Infrared Object Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

Abstract

Deploying target detection models on edge devices such as UAVs is challenging due to their limited size and computational capacity, while target detection models typically require significant computational resources. To address this issue, this study proposes a lightweight real-time infrared object detection model named LRI-YOLO (Lightweight Real-time Infrared YOLO), which is based on YOLOv8n. The model improves the C2f module’s Bottleneck structure by integrating Partial Convolution (PConv) with Pointwise Convolution (PWConv), achieving a more lightweight design. Furthermore, during the feature fusion stage, the original downsampling structure with ordinary convolution is replaced with a combination of max pooling and regular convolution. This modification retains more feature map information. The model’s structure is further optimized by redesigning the decoupled detection head with Group Convolution (GConv) instead of ordinary convolution, significantly enhancing detection speed. Additionally, the original BCELoss is replaced with EMASlideLoss, a newly developed classification loss function introduced in this study. This loss function allows the model to focus more on hard samples, thereby improving its classification capability. Compared to the YOLOv8n algorithm, LRI-YOLO is more lightweight, with its parameters reduced by 46.7% and floating-point operations (FLOPs) reduced by 53.1%. Moreover, the mean average precision (mAP) reached 94.1%. Notably, on devices with moderate computational power that only have a Central Processing Unit (CPU), the detection speed reached 42 frames per second (FPS), surpassing most mainstream models. This indicates that LRI-YOLO offers a novel solution for real-time infrared object detection on edge devices such as drones.