Comparison of Two Types YOLOv4-tiny Simplified Networks and Their Crack Detection Performance

Abstract:

To meet the demands of crack detection market in domestic tall buildings， taking the shortcomings of the fact that the existing YOLOv4-tiny deep network structure runs slowly on such edge devices as Raspberry Pi into account， two novel simplified YOLOv4-tiny deep network structures， that is， YOLOv4-lite1 and YOLOv4-lite2 were deduced by removing the second residual network， as well as adding a maxpool layer and changing the connection of the last route layer in this paper. The training set， the test set， and the verification set data of crack detection were then generated by using the crack pictures downloaded from the internet， and the training is conducted on a 64-bit Ubuntu16.04 system utilizing the Darknet deep learning framework. At the same time， the actual tests on the RaspberryPI 4B show that the YOLOv4-lite1 structure has a faster running speed， detection rate， and stability compared to the YOLOv4-lite2 structure. Finally， the next step of this related work is pointed out. The innovation of this research lies in further simplifying the YOLOv4-tiny network structure and the connection of the last layer route layer， thus obtaining two new YOLOv4-tiny deep network structures and better detection results.

Key words:intelligent detection;deep learning;edge device;deep neural networks