Object-class detection

Detector

Pedestrian

Engineering

Transport engineering

Object detection

Pure mathematics

Field (mathematics)

Systems engineering

Telecommunications

Computer science

Pedestrian detection

Physics

Task (project management)

Pattern recognition (psychology)

Mathematics

Optics

Object (grammar)

Focus (optics)

Face detection

Artificial intelligence

Deep learning

Feature extraction

Facial recognition system

Viola–Jones object detection framework

Computer vision

Object detection has seen many changes in algorithms to improve performance both on speed and accuracy. By the continuous effort of so many researchers, deep learning algorithms are growing rapidly with an improved object detection performance. Various popular applications like pedestrian detection, medical imaging, robotics, self-driving cars, face detection, etc. reduces the efforts of humans in many areas. Due to the vast field and various state-of-the-art algorithms, it is a tedious task to cover all at once. This paper presents the fundamental overview of object detection methods by including two classes of object detectors. In two stage detector covered algorithms are RCNN, Fast RCNN, and Faster RCNN, whereas in one stage detector YOLO v1, v2, v3, and SSD are covered. Two stage detectors focus more on accuracy, whereas the primary concern of one stage detectors is speed. We will explain an improved YOLO version called YOLO v3-Tiny, and then its comparison with previous methods for detection and recognition of object is described graphically.

YOLO v3-Tiny: Object Detection and Recognition using one stage improved model