Real-time vehicle overload detection method based on convolutional neural network

A real-time vehicle overload detection method based on a convolutional neural network (CNN), comprising:

• step 1:
• preparing a visual object classes (VOC) dataset, comprising:
• acquiring a plurality of images of each of vehicles with different axle numbers and wheelbases, and labeling each wheel and a vehicle body in each of the plurality of images using a labeling tool as the VOC dataset;
• step 2:
• configuring a training environment for a YOLO-V3 object detection network model, comprising:
• constructing the YOLO-V3 object detection network model with a darknet deep learning framework in a ubuntu system;
• step 3:
• training the YOLO-V3 object detection network model, comprising:
• training the YOLO-V3 object detection network model based on the VOC dataset using a YOLO-V3 object detection algorithm, and simplifying the YOLO-V3 object detection network model using a pruning algorithm;
• and
• step 4:
• uploading the trained YOLO-V3 object detection network model to a server, acquiring an image of a vehicle in real time by a camera and uploading the image to the server, detecting a number of axles and a relative wheelbase of the vehicle using the trained YOLO-V3 object detection network model, and obtaining a theoretical maximum load of the vehicle by comparing a detection result with a national vehicle load standard GB1589-2016;
• obtaining a true load of the vehicle through a piezoelectric sensor under a road;
• and determining whether the vehicle is overloaded by comparing the theoretical maximum load with the true load.