Asifa Mehmood Qureshi¹, Naif Al Mudawi², Mohammed Alonazi³, Samia Allaoua Chelloug⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3683-3701, 2024, DOI:10.32604/cmc.2024.043611

Abstract Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts. The extracted frames… More >

Ying Li^1,2, Guanghong Gong¹, Dan Wang¹, Ni Li^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2237-2265, 2023, DOI:10.32604/cmes.2023.025193

Abstract There are two types of methods for image segmentation. One is traditional image processing methods, which are sensitive to details and boundaries, yet fail to recognize semantic information. The other is deep learning methods, which can locate and identify different objects, but boundary identifications are not accurate enough. Both of them cannot generate entire segmentation information. In order to obtain accurate edge detection and semantic information, an Adaptive Boundary and Semantic Composite Segmentation method (ABSCS) is proposed. This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances. It includes adaptively dividing and modifying the… More > Graphic Abstract

Navleen S Rekhi^1,2,*, Jagroop S Sidhu², Amit Arora²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2791-2807, 2023, DOI:10.32604/csse.2023.026976

Abstract Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale… More >

Wei Chen¹, Caoyang Chen^1,*, Mi Liu¹, Xuhong Zhou², Haozhi Tan³, Mingliang Zhang⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 767-782, 2022, DOI:10.32604/cmc.2022.028571

Abstract The present paper proposes a detection method for building exterior wall cracks since manual detection methods have high risk and low efficiency. The proposed method is based on Unmanned Aerial Vehicle (UAV) and computer vision technology. First, a crack dataset of 1920 images was established using UAV to collect the images of a residential building exterior wall under different lighting conditions. Second, the average crack detection precisions of different methods including the Single Shot MultiBox Detector, You Only Look Once v3, You Only Look Once v4, Faster Regional Convolutional Neural Network (R-CNN) and Mask R-CNN methods were compared. Then, the… More >

Sandeep Kumar¹, Arpit Jain^2,*, Shilpa Rani³, Hammam Alshazly⁴, Sahar Ahmed Idris⁵, Sami Bourouis⁶

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 119-131, 2022, DOI:10.32604/iasc.2022.024812

Abstract The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems. Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes. It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle’s modest size, other objects with similar visual appearances are not correctly addressed. A robust algorithm for vehicle detection and classification has been proposed to overcome the limitation of… More >

Wei Chen¹, Mi Liu^1,*, Xuhong Zhou², Jiandong Pan³, Haozhi Tan⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3159-3174, 2022, DOI:10.32604/cmc.2022.026664

Abstract In construction, it is important to check whether workers wear safety helmets in real time. We proposed using an unmanned aerial vehicle (UAV) to monitor construction workers in real time. As the small target of aerial photography poses challenges to safety-helmet-wearing detection, we proposed an improved YOLOv4 model to detect the helmet-wearing condition in aerial photography: (1) By increasing the dimension of the effective feature layer of the backbone network, the model's receptive field is reduced, and the utilization rate of fine-grained features is improved. (2) By introducing the cross stage partial (CSP) structure into path aggregation network (PANet), the… More >

Yahia Said^1,2,*, Mohammad Barr¹, Taoufik Saidani^2,3, Mohamed Atri^2,4

Computer Systems Science and Engineering, Vol.40, No.2, pp. 607-618, 2022, DOI:10.32604/csse.2022.018479

Abstract Desertification has become a global threat and caused a crisis, especially in Middle Eastern countries, such as Saudi Arabia. Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification. The vegetation area in Makkah has been damaged because of desertification through wind, floods, overgrazing, and global climate change. The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area. In this paper, we propose an automatic desertification detection system based on Deep Learning techniques. Aerial images are classified using Convolutional Neural Networks… More >