Developing a More Reliable Framework for Extracting Traffic Data From a UAV Video

Li, Xiaohe; Wu, Jianping

doi:10.1109/tits.2023.3290827

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…YOLOv5-OBB-CR [22] algorithm improves the original C3 module into CReToNeXt module, and improves the original Loss function to SioU [4], effectively enhancing the network feature extraction ability. Both of the improved algorithms are applied to the detection of maintenance components in the YN92 marine diesel engine, the experimental results show that YOLOv5-OBB-CR algorithm has stronger feature extraction ability and higher detection accuracy in maintenance component detection compared to the original YOLOv5-OBB algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

An OBB Detection Algorithm of Maintenance Components Based on YOLOv5-OBB-CR

Ge,

Yang,

et al. 2024

ITC

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By detecting the position of maintenance components in real-time, maintenance guidance information can be superimposed and important operational guidance can be provided for maintenance personnel. The YOLOv5-OBB-CR real-time detection algorithm is proposed for maintenance component with orientation bounding box based on improved YOLOv5-OBB. The C3 module in the original network is improved to CReToNeXt, which can more effectively enhance the network's ability to learn image features. Considering that the network learning is the labeled rotation box information, the original Loss function CIoU is improved to SIoU with angle loss information, and the improved Loss function can more effectively describe the regression of the target box. The demonstration shows that the mAP@.5 0.95 of YOLOv5-OBB-CR-s (SIoU) is 85.6%, which is 6.7% higher than the original YOLOv5 OBB algorithm.

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Section: Introductionmentioning

confidence: 99%