Enhancing Thermal Infrared Object Detection Using SimAM-Integrated YOLOX for Improved Feature Representation
Jaehong Yoon, Heegwang Kim, Chanyeong Park, Junbo Jang, Jiyoon Lee, Joonki Paik · 2025
Object detection with thermal infrared images is vital for applications like autonomous driving in low-light and adverse environmental conditions. While TIRDet has improved thermal-only object detection through a Thermal-To-Visible (T2V) translation model, this module can suffer from information loss in certain scenarios. To address this, we propose enhancing the CSPDarknet53 backbone of YOLOX within TIRDet by incorporating SimAM, a parameter-free attention mechanism. SimAM enhances feature representation by inferring 3D attention weights based on an energy function inspired by neuroscience theories, without increasing model parameters. By strengthening feature extraction within the backbone, our approach compensates for the T2V module's information loss, thereby improving detection performance under challenging conditions. Experiments on the LLVIP and FLIR datasets demonstrate superior performance, underscoring the effectiveness of parameter-free attention mechanisms for advancing thermal object detection.