Enhancing YOLOv8 for Infrared Object Detection via Learnable Multi-Scale Context and Attention
TL;DR
This paper presents a simple yet effective architectural enhancement to YOLOv8 by replacing the standard Spatial Pyramid Pooling Fast module with a learnable multi-scale variant (SPPFPlus), and integrating the Convolutional Block Attention Module (CBAM) into both the backbone and neck.
Abstract
Infrared (IR) object detection poses unique challenges due to low texture, weak edges, sensor noise, and a strong dependence on global context. Modern convolutional object detectors, including YOLOv8, are primarily optimized for RGB imagery and often underperform when directly applied to large-scale infrared datasets. In this paper, we present a simple yet effective architectural enhancement to YOLOv8 by (1) replacing the standard Spatial Pyramid Pooling Fast (SPPF) module with a learnable multi-scale variant (SPPFPlus), and (2) integrating the Convolutional Block Attention Module (CBAM) into both the backbone and neck. The proposed approach introduces explicit parallel multi-scale context modeling and adaptive channel–spatial attention, addressing key representational limitations of infrared imagery. Extensive experiments on a large infrared dataset demonstrate an improvement of approximately 15% in detection performance over the YOLOv8 baseline, with notable gains in recall and small-object detection. The modifications are lightweight, modular, and can be seamlessly integrated into existing YOLOv8