Object detection is a complex computer vision task that involves identifying and locating the position of various elements inside a given image. It is more challenging than image classification, which can recognize different items but fails to discern their exact location. YOLO has become a popular choice among object detection methods due to its high accuracy and capability to process images in real time. This technique performs one forward propagation through the neural network to make predictions, followed by non-max suppression, to ensure that only each object is identified once. The output consists of the detected objects and bounding boxes associated with them. Additionally, YOLO uses a single convolutional neural network to predict multiple bounding boxes with associated class probabilities, increasing overall detection performance by training on full photographs.
Applications of the YOLO Algorithm in the Real World
- The YOLO algorithm has been adopted by police and surveillance systems to detect people or objects of interest in real time, prompting alerts and tracking motion.
- The algorithm is also versatile and effective for a number of research applications, such as recognizing movement in wildlife and identifying terrain from geographic data.
- YOLO models can identify traffic signals and lights, allowing autonomous vehicles to adjust to the environment and external objects.
- Activity recognition is another useful application of the YOLO algorithm, enabling computers to detect and recognize human activities, such as walking or running.
- This technology aids sports training programs or risk prevention in public spaces.
- Finally, using YOLO models, it is possible to estimate the 3D positions of people in images or video, which is useful in virtual and augmented reality applications.
