Infrared and visible image fusion through hybrid curvature filtering image decomposition

Liu, Guote; Zhou, Jinhui; Wu, Weiquan; Guo, Feng; Luo, Bing; Li, Licheng; Chen, Sijun

doi:10.1016/j.infrared.2021.103938

Cited by 9 publications

(1 citation statement)

References 30 publications

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“…On one front, several infrared object detection algorithms have improved performance through transfer learning [9][10][11][12]. Concurrently, multi-spectral detection algorithms combining visible and infrared imagery [13][14][15][16] have demonstrated noteworthy results on datasets like KAIST [17].…”

Section: Object Detection For Infraredmentioning

confidence: 99%

Infrared multi‐target detection and tracking in dense urban traffic scenes

Zha,

Luo,

2024

IET Image Processing

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Infrared object detection and tracking in dense urban traffic remain a challenge due to factors such as low contrast, small intra‐class differences, and frequent false positives and negatives. To overcome these, the authors introduce YOLO‐IR, an algorithm based on the enhanced YOLOv8s, and YOLO‐DeepOC‐IR, a comprehensive infrared multi‐object tracking method for urban traffic, integrating both detection and tracking. During preprocessing, three infrared image enhancement techniques, local contrast multi‐scale enhancement, non‐local means, and contrast limited adaptive histogram equalization, are applied for better reliability in dense scenes. To further improve the performance, the original YOLOv8s backbone is replaced with MobileVITv3 to enhance detection accuracy and robustness. This infrared feature extraction module, incorporated into the detector, combines canny edge detection, Gabor filtering, and open operation layers, significantly boosting object detection in infrared imagery. The tracker's feature processing capabilities are improved using the learned arrangements of three patch codes descriptor and locality‐sensitive hashing for feature extraction and matching. Experimental results on FLIR ADAS v2 and InfiRay datasets indicate superior performance of this method, achieving 78.6% mAP and 151.1 FPS in detection, and up to 80.8% moving object tracking accuracy, 78.6% identification F1 score, and 62.1% higher order tracking accuracy in multi‐object tracking.

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