ObjectDetection

Object detection is a computer vision task that involves identifying instances of semantic objects of a certain class (such as people, cars, or animals) within digital images or videos and locating them with bounding boxes or segmentation masks. It combines object localization and classification, producing both the object class and the coordinates of its bounding box.

Modern object detection systems typically generate a set of detections, each containing a class label, a confidence score, and spatial coordinates. Datasets used for training and benchmarking include COCO, PASCAL VOC, and Open Images, which provide images annotated with bounding boxes and class labels. Evaluation commonly uses metrics such as mean average precision (mAP) and Intersection over Union (IoU) thresholds to assess accuracy.

Approaches to object detection fall into two broad categories: two-stage detectors and single-shot detectors. Two-stage methods, exemplified by R-CNN, Fast R-CNN, and Faster R-CNN, first generate region proposals and then classify and refine them. Single-shot detectors, such as YOLO and SSD, predict bounding boxes and class probabilities in one pass, offering faster runtimes suitable for real-time applications. More recent methods incorporate feature pyramids and techniques to address class imbalance, such as RetinaNet and EfficientDet.

Common challenges include detecting small objects, handling occlusion and cluttered backgrounds, dealing with varying lighting, and meeting real-time constraints in video. Applications span autonomous driving, surveillance, robotics, and image search. Training requires annotated data and substantial compute, while deployment often involves model optimization techniques like quantization and pruning to balance accuracy, speed, and memory usage.