Ayesha¹, Muhammad Javed Iqbal¹, Iftikhar Ahmad^2,*, Madini O. Alassafi², Ahmed S. Alfakeeh², Ahmed Alhomoud³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3647-3661, 2023, DOI:10.32604/cmc.2023.020916 - 31 October 2022

Abstract Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System (ITS) and reduces road accidents. The major obstacles in automatic detection of tiny vehicles are due to occlusion, environmental conditions, illumination, view angles and variation in size of objects. This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area. In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) namely CenterNet. Substantially DNN disregards objects that are small in size 5 pixels and more false… More >

Stevica Cvetkovic^1,*, Nemanja Savic¹, Ivan Ciric²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 967-979, 2023, DOI:10.32604/iasc.2023.032526 - 29 September 2022

Abstract Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter. To achieve highly accurate results as well as provide real-time execution, we proposed a deep transfer learning approach over the state-of-the-art deep learning object detector. Our method, denoted as YOLOHANDS, is built on top of the You Only Look Once (YOLO) deep learning architecture, which is modified to adapt to the single class hand detection task. The model transfer is performed by modifying the higher convolutional layers including the last fully connected More >

Kisaezehra¹, Muhammad Umer Farooq^1,*, Muhammad Aslam Bhutto², Abdul Karim Kazi¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 911-927, 2023, DOI:10.32604/iasc.2023.031359 - 29 September 2022

Abstract The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain… More >

K. Venkatesh^1,*, S. Pasupathy¹, S. P. Raja²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 543-560, 2023, DOI:10.32604/iasc.2023.030701 - 29 September 2022

Abstract The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction. It takes an enormous number of times to analyze with the standardization and inter-observer variability. Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN). The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset. The proposed Faster R-CNN model is trained using the augmented dataset. For overcoming the Imbalanced Data problem, data augmentation techniques are imposed. The Faster R-CNN performance was compared with existing transfer learning techniques. The results show that the More >

Sukhendra Singh¹, Sur Singh Rawat², Manoj Gupta³, B. K. Tripathi⁴, Faisal Alanzi⁵, Arnab Majumdar⁶, Pattaraporn Khuwuthyakorn⁷, Orawit Thinnukool^7,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1673-1691, 2023, DOI:10.32604/cmc.2023.032364 - 22 September 2022

Abstract In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and… More >

Jun Wu, Penghui Fan, Yingxin Sun, Weifeng Gui^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1305-1321, 2023, DOI:10.32604/cmes.2022.020919 - 31 August 2022

Abstract Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss… More >

C. Narmadha¹, T. Kavitha², R. Poonguzhali², V. Hamsadhwani³, Ranjan walia⁴, Monia⁵, B. Jegajothi^6,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3613-3626, 2023, DOI:10.32604/iasc.2023.029323 - 17 August 2022

Abstract At present days, object detection and tracking concepts have gained more importance among researchers and business people. Presently, deep learning (DL) approaches have been used for object tracking as it increases the performance and speed of the tracking process. This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Annotation with ResNet based Faster regional convolutional neural network (R-CNN) named (AIA-FRCNN) model. The AIA-RFRCNN method performs image annotation using a Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR) called DCF-CSRT model. The AIA-RFRCNN model makes use… More >

Na Hyoun Kim, Namgyu Kim^*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2667-2682, 2023, DOI:10.32604/csse.2023.029732 - 01 August 2022

Abstract Object detection, one of the core research topics in computer vision, is extensively used in various industrial activities. Although there have been many studies of daytime images where objects can be easily detected, there is relatively little research on nighttime images. In the case of nighttime, various types of noises, such as darkness, haze, and light blur, deteriorate image quality. Thus, an appropriate process for removing noise must precede to improve object detection performance. Although there are many studies on removing individual noise, only a few studies handle multiple noises simultaneously. In this paper, we More >

P. D. Sheena Smart^1,*, K. K. Thanammal², S. S. Sujatha²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2065-2080, 2023, DOI:10.32604/csse.2023.028053 - 01 August 2022

Abstract In object detection, spatial knowledge assisted systems are effective. Object detection is a main and challenging issue to analyze object-related information. Several existing object detection techniques were developed to consider the object detection problem as a classification problem to perform feature selection and classification. But these techniques still face, less computational efficiency and high time consumption. This paper resolves the above limitations using the Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves the accuracy of object detection with minimum time. The proposed method uses a multilayer for finding the similarity score. A fuzzy membership More >

S. Prabu^1,*, J. M. Gnanasekar²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2257-2271, 2023, DOI:10.32604/iasc.2023.029877 - 19 July 2022

Abstract Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video,… More >