Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection

• Published in: International journal of computational intelligence systems Vol. 16; no. 1; pp. 1 - 29
• Main Authors: Sirisha, U., Praveen, S. Phani, Srinivasu, Parvathaneni Naga, Barsocchi, Paolo, Bhoi, Akash Kumar
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 02.08.2023; Springer Nature B.V; Springer
• Subjects: Artificial Intelligence; Artificial neural networks; Computational Intelligence; Computer vision; Control; Darknet; Deep learning; Detectors; Engineering; Machine learning; Mathematical Logic and Foundations; Mechatronics; Object detection; Object recognition; Performance analysis; Performance measurement; Review Article; Robotics; Statistical analysis; YOLO; YOLO; Deep learning; Darknet; Performance analysis; Object detection
• ISSN: 1875-6883; 1875-6891; 1875-6883
• DOI: 10.1007/s44196-023-00302-w

Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases.