Object Detection Algorithm for High Resolution Images Based on Convolutional Neural Network and Multiscale Processing

Abstract

In this article we propose an effective algorithm for small object detection in high resolution images. We look at the image at different scales and use block processing by convolutional neural network. Pyramid layers number is defined by input image resolution and convolutional layer size. On each layer of pyramid except the highest we perform splitting overlapping blocks to improve small object detection accuracy. Detected areas are merged into one if they belong to the same class and have high overlapping value. In the paper experimental results using YOLOv4 for 4K and 8K images are presented. Our algorithm shows better detecting small objects results in high-definition video than YOLOv4.

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

“…It has a small model magnitude, a rapid detection speed, and is better suited to being promoted to some edge or mobile end devices. The technique proposed by Bohusha et al [40] for recognizing objects in 4K and 8K images and it has a great efficiency in recognizing small objects in 4K and 8K quality images. Kadadi et al [41] shows how to use the background subtraction (BGS) method to find and follow the intended moving objects (MOs).…”

High-definition technology of AI inference scheme for object detection on edge/terminal

“…Large objects in the input image can be detected, but small objects are difficult to detect because the characteristic parts for identifying the objects are also shrunk. Dividing the input image into several parts of a limited size can also be done to prevent shrinkage of the characteristic parts [21,22,23,24,25,26], but this means large objects that straddle the divided images cannot be detected because the characteristic parts are also divided. As another approach, a coarse-to-fine-based inference scheme for object detection has been proposed [27,28].…”