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This article will explain in detail how to use YOLOv5 to achieve real-time target detection for multi-channel cameras. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.

Preface

YOLOV5 model is a hot target detection model from its release to now, and it has been widely used in various scenarios. Therefore, we need to know not only how to train the yolov5 model, but also how to deploy applications. In this blog, I will use the yolov5 model to simply implement the deployment application demo from the camera end to the web side, and provide some deployment ideas for readers.

First, the strength of YOLOV5

The difference between you and the target detection master is a YOLOV5 model. YOLOV5 can be said to be a model that applies almost all target detection tricks to one. Many mainstream methods of data enhancement, model training and model post-processing can be found in it. Let's briefly summarize the methods used by yolov5:

Features added by yolov5:

Tricks for yolov5 training and forecasting:

Live stream 2

At this point, we have written the real-time inference code for multi-channel cameras using the YOLOv5 model, and let's start running:

-run it directly under the following directory in the terminal:

Python web_main.py

Then, the following message appears in the terminal:

[INFO] YOLOv5 Config: {'source':' streams.txt', 'weights':' runs/train/exp/weights/best.pt', 'device':' cpu', 'imgsz': 640,' stride': 32, 'conf_thres': 0.35,' iou_thres': 0.45, 'augment': False} Fusing layers...1/2: rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov... Success (240x160 at 24.00 FPS) .2Accord 2: rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov... Success (240x160 at 24.00 FPS). * Serving Flask app "web_main" (lazy loading) * Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Debug mode: off * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)

* then open the browser, and after entering localhost:5000, the terminal does not report any errors, and the following page appears:

This is the end of the article on "how to use YOLOv5 to achieve real-time target detection for multi-channel cameras". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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