<div>Object detection is a considerable area of computer vision. The aim of object detection is to increase its efficacy and accuracy that have always been targeted. The research area of object detection has many broad areas, include self-driving, manufacturing and retail stores. However, scenes of using object detection in detecting dense objects have rarely gathered in much attention. Dense and small object detection is relevant to many real-world scenarios, for example, in retail stores and surveillance systems. Human suffers the speed and accuracy to count and audit the crowded product on the shelves. We motivate to detect the dense product on the shelves. It is a research area related to industries. In this thesis, we going to fine-tune CenterNet as a detector to detect the objects on the shelves. To validate the effectiveness of CenterNet network architecture, we collected the Bottle dataset that collected images from real-world supermarket shelves in different environments. We compared performance on the Bottle Dataset with many different circumstances. The ResNet-101(colored+PT) achieved the best result of CenterNet that outperform other network architectures. we proved perspective transformation can be implemented on state-of-the-art detectors, which solved the issue when detector did not achieve a good result on strongly angled images. We concluded that colored information did contribute to the performance in detecting the objects on the shelf, but it did not contribute as much as geometric information provided for learning its information. The result of the accuracy of detection on CenterNet meets the need of accuracy on industry requirements.</div><div><br></div>