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

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.

Automated summary

Object detection is a critical problem in computer vision, and deep neural networks have greatly improved its performance. There are two types of object detectors: two-stage and one-stage. Two-stage detectors use a complex architecture for detection, while one-stage detectors detect all potential regions in a single shot. Both detection accuracy and inference speed are important factors when evaluating object detectors. Two-stage detectors generally have higher detection accuracy, but YOLO and its predecessors have improved accuracy significantly. In some cases, the speed of YOLO detectors is more important. This study explores performance metrics, regression formulations, and single-stage detectors for YOLO. It also discusses various YOLO variations and their design, performance, and use cases.