An Effective Object Detection Algorithm for High Resolution Video by Using Convolutional Neural Network

Vorobjov, Denis; Zakharava, I. Yu.; Bohush, Rykhard; Ablameyko, Sergey

doi:10.1007/978-3-319-92537-0_58

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Generally, most of the image object detection algorithms for high definition images are carried out using two principals: image dividing into blocks and CNNs application for each block. In [3], the algorithm for the accurate of small objects detection in high-definition video are presented. Each image is separated into overlapping blocks and object detection in each block is performed with CNN YOLO.…”

Section: Related Workmentioning

confidence: 99%

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

Bohush¹,

Ablameyko²,

Ihnatsyeva³

et al. 2021

Computer Modeling and Intelligent Systems

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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.

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Section: Related Workmentioning

confidence: 99%

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

Bohush¹,

Ablameyko²,

Ihnatsyeva³

et al. 2021

Computer Modeling and Intelligent Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Uzawa

Yoshida

Iinuma

et al. 2023

IEICE Electron. Express

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To detect a wide range of objects with one camera at once, real-time object detection in high-definition video is required in video artificial intelligence (AI) applications for edge/terminal, such as beyond-visual-line-of-sight (BVLOS) drone flight. Although various AI inference schemes for object detection (e.g., you-only-look-once (YOLO)) have been proposed, they typically have limitations on the input image size and thus need to shrink the input high-definition image down to the limit. This makes small objects collapsed and undetectable. This paper presents our proposal technology for solving this problem and its effective implementation, where multiple object detectors cooperate to detect small and large objects in high-definition video such as full HD and 4K.

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“…Large objects in the input image can be detected, but small objects are difficult to detect because the characteristic parts for identifying objects are also shrunk. Dividing the input image into a limited size can also be considered to prevent shrinking 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. In other words, the conventional approaches are unsuitable for object detection in highdefinition images.…”

Section: Introductionmentioning

confidence: 99%

High-definition object detection technology based on AI inference scheme and its implementation

Uzawa

Yoshida

Iinuma

et al. 2021

IEICE Electron. Express

View full text Add to dashboard Cite

Video artificial intelligence (AI) applications for edges/terminals, such as non-visual drone flight, require object detection in high-definition video in order to detect a wide range of objects with one camera at once. To enable satisfying this requirement, we propose a new high-definition object detection technology based on an AI inference scheme and its implementation. In the technology, multiple object detectors cooperate to detect small and large objects in high-definition video. The evaluation results show that our technology can achieve 2.1 times higher detection performance in full HD images thanks to the cooperation of three object detectors.

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An Effective Object Detection Algorithm for High Resolution Video by Using Convolutional Neural Network

Cited by 12 publications

References 12 publications

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

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

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

High-definition object detection technology based on AI inference scheme and its implementation

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