YOLO-MS: Multispectral Object Detection via Feature Interaction and Self-Attention Guided Fusion

Xie, Yumin; Zhang, Langwen; Yu, Xiaoyuan; Xie, Wei

doi:10.1109/tcds.2023.3238181

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…Modality mAP Faster R-CNN [21] R\I 63.60\75.30% HalfwayFusion [29] R+I 71.17% DALFusion [30] R+I 72.11% CFR [29] R+I 72.39% GAFF [31] R+I 73.80% YOLO-MS [32] R+I 75.20% MFF-YOLOv5 [15] R+I 78.20% UA-CMDet [19] R+I 78.60% FFODNet(ours) R+I 78.30%…”

Section: Methodsmentioning

confidence: 99%

Multimodal Object Detection of UAV Remote Sensing Based on Joint Representation Optimization and Specific Information Enhancement

Wang,

Xu,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Methodsmentioning

confidence: 99%

Multimodal Object Detection of UAV Remote Sensing Based on Joint Representation Optimization and Specific Information Enhancement

Wang,

Xu,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Yun et al [37] proposed Infusion-Net based on high-frequency information acquisition and YOLOv7 algorithm. Xie et al [38] designed a multispectral detection algorithm, YOLO-MS, based on the YOLOv5 framework and attention-guided fusion.…”

Section: Multispectral Object Detectionmentioning

confidence: 99%

“…To validate the superiority of our proposed algorithm, we conducted experiments on the FLIR_Aligned and M3FD datasets, two widely used datasets for dual-mode road object detection. The performance of our algorithm, MRD-YOLO, was compared with that of state-of-the-art dual-mode algorithms, such as SuperYOLO [42] and YOLO-MS [38]. The experimental results presented in Tables 3 and 4 demonstrate that even when compared with these advanced dual-mode detection algorithms, MRD-YOLO exhibits superior performance.…”

Section: Comparison Of Different Detectorsmentioning

confidence: 99%

MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes

Sun,

Chen,

Qiu

et al. 2024

Sensors

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Object detection is one of the core technologies for autonomous driving. Current road object detection mainly relies on visible light, which is prone to missed detections and false alarms in rainy, night-time, and foggy scenes. Multispectral object detection based on the fusion of RGB and infrared images can effectively address the challenges of complex and changing road scenes, improving the detection performance of current algorithms in complex scenarios. However, previous multispectral detection algorithms suffer from issues such as poor fusion of dual-mode information, poor detection performance for multi-scale objects, and inadequate utilization of semantic information. To address these challenges and enhance the detection performance in complex road scenes, this paper proposes a novel multispectral object detection algorithm called MRD-YOLO. In MRD-YOLO, we utilize interaction-based feature extraction to effectively fuse information and introduce the BIC-Fusion module with attention guidance to fuse different modal information. We also incorporate the SAConv module to improve the model’s detection performance for multi-scale objects and utilize the AIFI structure to enhance the utilization of semantic information. Finally, we conduct experiments on two major public datasets, FLIR_Aligned and M3FD. The experimental results demonstrate that compared to other algorithms, the proposed algorithm achieves superior detection performance in complex road scenes.

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Efficient cross-modality feature interaction for multispectral armored vehicle detection

Zhang,

Chang,

Zhao

et al. 2024

Applied Soft Computing

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YOLO-MS: Multispectral Object Detection via Feature Interaction and Self-Attention Guided Fusion

Cited by 5 publications

References 40 publications

Multimodal Object Detection of UAV Remote Sensing Based on Joint Representation Optimization and Specific Information Enhancement

Multimodal Object Detection of UAV Remote Sensing Based on Joint Representation Optimization and Specific Information Enhancement

MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes

Efficient cross-modality feature interaction for multispectral armored vehicle detection

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