A Novel Signal Detection Method Based on Multivariate Test Statistics for Ambient Backscatter Communications

Wang, Jing; Ren, Pinyi; Xu, Dongyang; Lü, Lei

doi:10.1109/iccc55456.2022.9880631

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…At present, object detection is being well applied in the field of computer vision [3][4][5], and it has become possible to detect bridge diseases in different environments. It worth noting that object detection is different from the deep learning based automatic modulation classification [6,7], specific emitter identification [8][9][10] and malware traffic classification [11,12].…”

Section: Introductionmentioning

confidence: 99%

Small target disease detection based on YOLOv5 framework for intelligent bridges

Zhang,

Xiong,

Jiang

et al. 2024

Peer-to-Peer Netw. Appl.

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This paper proposes a small target disease detection method using YOLOv5 framework for detecting small apparent diseases on intelligent bridges, aiming to address the problem of missed and false detection. To enhance the detection of small apparent diseases, a layer for detecting small objects is added to the YOLOv5 model. Additionally, an ECA attention mechanism module is embedded in the feature enhancement network to improve the extraction of disease features. To validate the effectiveness of the proposed algorithm, a dataset of 996 bridges with apparent diseases such as corrosion, rebar, speckle, hole and spall was established and trained after manual annotation and data augmentation. The experiment showed that the proposed algorithm achieves a mAP of 87.91%. Compared to the original YOLOv5 model, the proposed algorithm improved the mAP on the bridge apparent disease dataset by 1.97%. This algorithm accurately detects small apparent diseases on bridges and effectively reduces missed detection.

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