Fine-Grained Object Detection Using Transfer Learning and Data Augmentation

Abstract

Object detection plays a vital role in many real-world computer vision applications such as self-driving cars, human-less stores and general purpose robotic systems. Convolutional Neural Network(CNN) based Deep Learning has evolved to become the backbone of most computer vision algorithms, including object detection. Most of the research has focused on detecting objects that differ significantly e.g. a car, a person, and a bird. Achieving fine-grained object detection to detect different types within one class of objects can be crucial in tasks like automated retail checkout. This research has developed deep learning models to detect 200 types of similar birds. The models were trained and tested on CUB-200-2011 dataset. To the best of our knowledge, by attaining a mean Average Precision (mAP) of 71.5% we achieved an improvement of 5 percentage points over the previous best mAP of 66.2%.