Akademik Veri Yönetim Sistemi
14th International Conference on Image Processing, Theory, Tools and Applications, IPTA 2025, İstanbul, Türkiye, 13 - 16 Ekim 2025, (Tam Metin Bildiri)
Image fusion is a technique that integrates information from multiple sources into a single image to produce richer and more meaningful visual content. This study applied three classical fusion methods - Multi-Scale Transform (MST), Saliency-Based fusion, and Anisotropic Diffusion with KL Transform - to RGB and thermal image pairs from the LLVIP dataset. CLAHE preprocessing was applied to enhance contrast, and the resulting fusion images were evaluated in terms of object detection performance and image quality metrics. For object detection, YOLOv4, YOLOv4-Tiny, and YOLOv7 models were used. According to YOLOv4 results, MST fusion applied to CLAHE-preprocessed RGB images achieved 0.18% higher mAP @ 0.5 accuracy compared to thermal images alone and 9.53% higher accuracy compared to RGB images alone. Regarding the authors' knowledge, this is the first time the study presents the comparative analysis of these models on the LLVIP dataset. The fusion outputs were also evaluated using SSIM, Mutual Information, and Qab/F metrics. The results indicate that the Saliency-Based fusion method yielded the best performance, particularly regarding visual quality.