Performance Evaluation on Real-time object detection using DL techniques - Sai Jyothi, B; Saathvika Durga Abhinaya, Chavali; Lahari, Bellamkonda; Devika Priya, Chinta; Anjali, Devarapalli; Sri Navya, Bathula (International Journal of Computer Sciences and Engineering, 2024)
Objects are located by drawing a bounding box around the detected object. One of computer vision`s specialties is object detection, which finds things in an image or video. Techniques for object detection are the foundation of the area of artificial intelligence. Typically, object detection uses deep learning and machine learning to yield accurate and significant findings. It is essentially made up of localization and classification. The state-of-the-art techniques utilized for real-time object detection have advanced recently. This study paper compares state-of-the-art techniques, such as faster region convolutional neural networks (Faster R-CNN) and you only look once V8 (YOLOV8). These algorithms are deep neural network representations, or neural networks with numerous hidden layers. Although each of these algorithms is notable for its own distinctiveness, they are compared to see which is superior. This study focuses on determining which of these algorithms is more practical to employ despite sharing a common core, namely CNNs.