Rethinking the Object Detection
Rethinking the Object Detection


Object localization/detection is one of the most crucial tasks in artificial intelligence and computer vision. The importance of object detection is that most of the vision tasks start with object localization. Real-world applications such as autonomous driving, personal/industrial robotics, person counting, object tracking, surveillance, OCR (optical character recognition) need to localize the object in the given image or video.

Object detection has a long history. The researchers have been continuously working on improving object detection techniques. Before the deep learning era, hand-crafted features — haar-like features, HOGs (histogram of gradients), and deformable part models — were used to train an object localization classifier. With the great success of deep learning in computer vision, novel deep learning-based object detection methods (features extracted from deep convolutional neural networks) have been proposed. There are various very robust and high performing object detection methods which help to boost real-world applications.


Alisher Abdulkhaev is a Machine Learning Engineer working for Browzzin — AI powered Social Fashion App. Alisher is the co-director and board member at Machine Learning Tokyo—award winning non profit organization dedicated to democratizing machine learning.