Object Detection Methods Review
Learn object detection principles, YOLO architecture, and key techniques: adjusting anchors, IoU, loss functions, and non-maximum suppression for accuracy.
Let's review various object detection methods in traditional and deep learning (DL) approaches, exploring their applications and significance across diverse fields. Understanding the different methods of object detection is pivotal, as it serves as a foundational technology with wide-ranging implications in diverse fields.
In the context of object detection algorithms, we distinguish between traditional methods and the dominating methods of deep learning.
Traditional methods
Before the surge of deep learning, traditional object detection methods laid the foundation. These methods employ algorithmic approaches, utilizing techniques such as clustering candidate regions and deriving distinctive features. The process involves proposing regions of interest, followed by a systematic classification of these regions based on their features. While effective in certain scenarios, traditional methods often face challenges in handling complex visual data and achieving high accuracy.
Deep learning methods
Deep learning (DL) methods have long been at the forefront of object detection, offering significant advancements. These methods can be broadly categorized into single-shot detectors (one-stage detectors) and two-stage detectors. Single-shot detectors, exemplified by YOLO (You Only Look Once) and Convolutional SD, aim to detect objects directly in a single pass. All of these methods are based on the convolutional operation.
The general architecture of two-stage object detection methods comprises two primary steps: region proposals and subsequent classification i.e., suggesting regions and then figuring out what's in ...