Improvement of Detection Rate for Small Objects Using Pre-processing Network

Abstract

Artificial intelligence (AI) has been developing in a variety of methods over the past decade. However most AI experts worried to build a deep or wide network because the accuracy of AI models depends heavily on the depth of the network. In general deep and wide networks are better at learning than those that are less deep and wide and wide. On the other hand deeper networks are more complex and have many disadvantages such as computational cost and system specification dependency. We propose a novel method to improve the average recall rate for small objects in the deep convolutional network in the paper. The proposed method added pre-processing layer before the network rather than stacking the networks deeper or wide. The presented pre-processing layer consists of two major parts: up-sampling and down-sampling of the data. The overall objective of up-sampling and down-sampling is to enhance the resolution of small objects in the input image. The pre-processing network improves the average recall rate of the base network to 3.56%. This experiment result depicts that the proposed method outperforms the small object detection performance. © 2021 ACM.