Object Detection in Security Inspection Scenarios Based on YOLOv5s: Exploring Experiments

Abstract

At present, there are many problems with the common methods of manual safety checks. Applying object detection technology to X-ray security inspection can effectively improve efficiency and reduce operating costs. The training parameters of deep neural networks directly affect the performance of the network. Based on YOLOv5s network model, this paper uses the method of control variables to explore the impact of training strategies, data enhancement strategies, and loss function selection on network performance. The experimental results show that a suitable batch size and learning rate can achieve significant performance improvements in the model without changing the network structure. With the increase in strength of the data enhancement strategy, the model performance shows a trend of first improving and then decreasing. The selection of the loss function has a great impact on the model’s performance. The research methods and ideas in this article can provide inspiration for subsequent related research.

Sequential Learning of Flame Objects Sorted by Size for Early Fire Detection in Surveillance Videos

“…YOLOv5n uses CSPDarknet with three convolutional layers in the backbone, as well as cross-stage partial networks, for feature extraction from an image, following the approach in [30,31]. YOLOv5n employs both binary cross-entropy and logistic loss functions in the loss function computation, resulting in higher accuracy and contributing to improved efficiency of the model in detection due to a reduced number of parameters in the neck network compared to YOLOv5s, a lightweight model version of YOLOv5 [32].…”