Cleanliness recognition of construction site vehicles based on the YOLOv8 object detection model
To alleviate the urban road pollution and hygiene problems caused by construction site vehicles leaving the site without being cleaned,a vehicle cleaning detection algorithm was designed using a target detection method in the field of computer vision,i.e.,YOLOv8 technology.The algorithm first constructed object detection labels and cleanliness labels based on actual video data from construction sites,and then used the constructed dataset for training the YOLOv8 object detection model specific to vehicle washing.The trained model was then employed for real-time detection of target vehicles and the water spraying situation of cleaning devices.Finally,the detected results were used to calculate the ultimate cleanliness status in real time.For the cleaning device sometimes with a small water spray area,an additional detection layer dedicated to small-sized targets was introduced into the network architecture,and the focus layer technology was used to perform block processing on the input image.Validation metrics included precision,recall,mean average precision,frames per second,and the final cleanliness level.The results showed that the algorithm achieved an accuracy of 97.6％in determining the vehicle washing situation,indicating that the proposed improved model effectively solved the problem of site vehicle cleanliness recognition.
万方数据
维普
