Deep learning

Physics

Pattern recognition (psychology)

Pure mathematics

Mathematics

Optics

Engineering

Artificial intelligence

Systems engineering

Viola–Jones object detection framework

Focus (optics)

Pedestrian

Transport engineering

Object detection

Facial recognition system

Pedestrian detection

Computer science

Task (project management)

Feature extraction

Object (grammar)

Field (mathematics)

Computer vision

Face detection

Object-class detection

Telecommunications

Detector

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