A novel model for higher performance object detection with deep channel attention super resolution

IF 5.1 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech Pub Date : 2025-02-25 DOI:10.1016/j.jestch.2025.102003

Ayse Berika Varol Malkocoglu, Ruya Samli

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引用次数: 0

Abstract

With the introduction of deep learning methods, target object detection studies have gained momentum and started to be used in many areas. In recent years, various problems related to training the model with appropriate images and improving the current image quality to detect the target object more successfully have been discussed. In the light of these discussions, the identification of dangerous objects, which is one of the most important areas of object detection, is quite remarkable. For this reason, within the scope of study, a new dataset called DMGDATA-mini was prepared to detect dangerous objects and presented to the public. By using the data in the training of the You Only Look Once (YOLO) model of YOLOv5, it was ensured that the model successfully detected the object. In order to increase the object detection success and observe the results, popular Super Resolution (SR) algorithms and the Deep Channel Attention Super Resolution (DCASR) algorithm developed by us were integrated into the structure and the effect of SR algorithms on object detection was observed. It was determined with the Peak Signal-to-Noise Ratio (PSNR) metric that the developed DCASR algorithm performs better image enhancement than all SR algorithms in the study. Also, YOLOv5 and YOLOv5 + SR models were also compared. Thanks to the improved DCASR model, a 9.9 % increase in object detection success was observed. It was observed that SR algorithms have a positive effect on performance in general.

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来源期刊

Engineering Science and Technology-An International Journal-Jestech Materials Science-Electronic, Optical and Magnetic Materials

CiteScore

11.20

自引率

3.50%

发文量

153

审稿时长

22 days

期刊介绍： Engineering Science and Technology, an International Journal (JESTECH) (formerly Technology), a peer-reviewed quarterly engineering journal, publishes both theoretical and experimental high quality papers of permanent interest, not previously published in journals, in the field of engineering and applied science which aims to promote the theory and practice of technology and engineering. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews and communications in the broadly defined field of engineering science and technology. The scope of JESTECH includes a wide spectrum of subjects including: -Electrical/Electronics and Computer Engineering (Biomedical Engineering and Instrumentation; Coding, Cryptography, and Information Protection; Communications, Networks, Mobile Computing and Distributed Systems; Compilers and Operating Systems; Computer Architecture, Parallel Processing, and Dependability; Computer Vision and Robotics; Control Theory; Electromagnetic Waves, Microwave Techniques and Antennas; Embedded Systems; Integrated Circuits, VLSI Design, Testing, and CAD; Microelectromechanical Systems; Microelectronics, and Electronic Devices and Circuits; Power, Energy and Energy Conversion Systems; Signal, Image, and Speech Processing) -Mechanical and Civil Engineering (Automotive Technologies; Biomechanics; Construction Materials; Design and Manufacturing; Dynamics and Control; Energy Generation, Utilization, Conversion, and Storage; Fluid Mechanics and Hydraulics; Heat and Mass Transfer; Micro-Nano Sciences; Renewable and Sustainable Energy Technologies; Robotics and Mechatronics; Solid Mechanics and Structure; Thermal Sciences) -Metallurgical and Materials Engineering (Advanced Materials Science; Biomaterials; Ceramic and Inorgnanic Materials; Electronic-Magnetic Materials; Energy and Environment; Materials Characterizastion; Metallurgy; Polymers and Nanocomposites)