Written in the front:
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This project is running on the Ubuntu system, please see another article in the column for running on the Windows system. The project code and data set are attached at the end of the article. The detection and recognition results are as follows:
1 Introduction
The helmet wearing detection system based on yolov7 usually includes the following components:
Data Collection and Preparation: Data needs to be collected and prepared from various data sources such as images, video or sensors. This usually includes operations such as image or video capture, annotation, cropping, and scaling. In this project, images with and without hard hats need to be collected.
Object recognition algorithm selection: It is necessary to select an appropriate object recognition algorithm according to the actual application scenario, such as YOLO, SSD, Faster R-CNN, etc., to meet different performance, accuracy and speed requirements.
Object detection model training: Use the collected data to train the object detection model. During the training process, it is necessary to consider adjusting the hyperparameters of the model, such as learning rate, batch size, regularization, etc. In addition, it is necessary to preprocess, enhance and expand the training data to enhance the generalization ability of the model.
Model evaluation and optimization: After model training, the model needs to be evaluated and optimized. Evaluation can be carried out by calculating indicators such as accuracy rate, recall rate, F1 score, and AP (average precision). Optimization can be achieved through techniques such as fine-tune, model compression, and quantization.
Model deployment and application: After completing model training and evaluation, it needs to be deployed into practical applications. this is usually