Study on Improved YOLO_v3-based Algorithm for Identifying Open Windows on Building Facades

Abstract

In order to ensure the security of building facades in key areas and improve the security detection efficiency of security personnel on building facades, this paper proposes an improved YOLO v3-based algorithm for open window detection recognition in building facade images, which extracts open window features from images to make predictions on full images by convolutional neural network. Firstly, due to the absence of publicly available window datasets in the network and the high number of window types that exist in reality, a self-constructed window dataset containing 13573images of open windows is used to train and test the window detection model. The data set is then clustered by the K-Means clustering algorithm to select an Anchor Box more suitable for window detection, which draws on the ShuffleNet idea to strengthen the feature extraction method, and then optimizes the network structure of YOLO v3. Finally, a block detection mechanism is introduced to effectively enhance the network’s ability to detect small dense targets; experimental results show that the method improves the accuracy and speed of window detection and reduces the workload of security personnel in key areas to manually check for windows on both sides of the street. Fire detection, missing and falling bricks on the floor, and overhead throw detectionare of great importance.