Conforming to the rapidly increasing market demand of crack detection for tall buildings, the idea of integrating deep network technology into wall climbing robot for crack detection is put forward in this paper. Taking the dependence and hardware requirements when deployed on such edge devices as Raspberry Pi into consideration, the Darknet neural network is selected as the basic framework for detection. In order to improve the inference efficiency on edge devices and avoid the possible premature over-fitting of deep networks, the lite YOLOv4-tiny algorithm is then improved from the original YOLOv4-tiny algorithm and its structure is illustrated using Netron accordingly. The images downloaded from Internet and taken from the buildings in campus are processed to form crack detection data sets, which are trained on personal computer with the AlexeyAB version of Darknet to generate weight files. Meanwhile, the AlexeyAB version of Darknet accelerated by NNpack package is deployed on Raspberry Pi 4B, and the crack detection experiments are carried out. Some characteristics, e.g., fast speed and lower false detection rate of the lite YOLOv4-tiny algorithm, are confirmed by comparison with those of original YOLOv4-tiny algorithm. The innovations of this paper focus on the simple network structure, fewer network layers, and earlier forward transmission of features to prevent over-fitting, showing the new lite neural network exceeds the original YOLOv4-tiny network significantly.
SONG Liboa (宋立博), FEI Yanqiongb (费燕琼)
. New Lite YOLOv4-Tiny Algorithm and Application on Crack Intelligent Detection[J]. Journal of Shanghai Jiaotong University(Science), 2024
, 29(3)
: 528
-536
.
DOI: 10.1007/s12204-022-2504-8
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