OpenCV: Issues with Real-Time Object Detection Using YOLOv5 on Raspberry Pi 4
I'm refactoring my project and I've been banging my head against this for hours. I've been struggling with this for a few days now and could really use some help. I've been struggling with this for a few days now and could really use some help. I'm working on a project that involves real-time object detection using YOLOv5 with OpenCV on a Raspberry Pi 4. I've set everything up according to the YOLOv5 documentation, including installing the necessary dependencies like `opencv-python` and `torch`. However, I am facing performance issues, with frame rates dropping below 5 FPS when trying to process video from the Raspberry Pi camera. Here's a snippet of the code I am using to run inference on the video stream: ```python import cv2 import torch # Load the YOLOv5 model model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) # Initialize the video capture cap = cv2.VideoCapture(0) while cap.isOpened(): ret, frame = cap.read() if not ret: break # Perform inference results = model(frame) # Display the results cv2.imshow('YOLOv5 Detection', results.render()[0]) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ``` I've tried reducing the image size before passing it to the model to improve performance: ```python frame = cv2.resize(frame, (640, 480)) ``` Despite this, the frame rate is still unacceptably low, and I also see delays in object detection, which is quite frustrating. I've checked that the model is running in inference mode, and I’ve tried different versions of OpenCV, ensuring I have the latest one. Additionally, I've disabled any unnecessary GUI elements to minimize processing overhead. The model outputs are sometimes inconsistent, with some objects not being detected at all, even when they are clearly visible in the frame. My Raspberry Pi has 4GB of RAM, and I'm running the latest Raspbian OS. Are there any optimizations I can do specifically for the Raspberry Pi environment, such as using a smaller model or adjusting specific parameters? Any insights or suggestions would be greatly appreciated. This is part of a larger application I'm building. This is happening in both development and production on Windows 11. Any ideas what could be causing this? This is part of a larger microservice I'm building. Could this be a known issue? I'm on Debian using the latest version of Python. Am I approaching this the right way? For context: I'm using Python on Windows 10. I'd be grateful for any help.