Object Detection Learning Path

Definition and scope of object detection

Object detection is a computer vision and image processing technology focused on identifying instances of semantic object classes (such as humans, buildings, or cars) in digital images and videos. It is closely related to tasks like face detection and pedestrian detection, and it supports a wide range of real-world computer vision applications, including image retrieval and video surveillance. The scope of object detection includes not only locating objects but also classifying them, often using bounding boxes to represent object positions. It is commonly used in tasks such as image annotation, vehicle counting, activity recognition, and face recognition, and it can be integrated with tracking to follow objects over time (e.g., a ball in sports footage or a person in a video). Because training and test data may come from different distributions, the task can be harder due to domain gaps, motivating approaches such as unsupervised domain adaptation to improve performance across domains.

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Object classes and instance detection in images

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