Bird Detection using OpenCV. Overview This project uses OpenCV to detect birds in images or video streams using classical computer vision techniques such as background subtraction and contour detection.
OpenCV provides a simple way to implement object detection using Haar Cascades a classifier trained to detect objects based on positive and negative images. In this article we will focus on detecting objects using it which is simple and effective for real-time object detection.
Import OpenCV. Define Bird Detection Function. Load Video and Bird Image. Process Video Frame by Frame. Annotate and Display Results.
Using OpenCV and YOLOv8, I built a system capable of detecting birds and drones directly from images . The idea was simple: Work only with static images (no video). Use the lightweight YOLOv8 Nano model for faster performance. Display the detection results directly with OpenCV.
In this project we used two NCS for bird detection and classification. One for analyze and detect (TINY YOLO) and another for classification of the detected bird (GoogLeNet). Pre-training for detection of birds (i.e. trained dataset) is done using TINYYOLO.
Furthermore, visual representations like the one above help us fully grasp the concept of Detection Of Birds And Apes Images Using Opencv.
For better accuracy, use binary images. So before finding contours, apply threshold or canny edge detection.In OpenCV, finding contours is like finding white object from black background.
