Saeed Masoud Alshahrani¹, Saud S. Alotaibi², Shaha Al-Otaibi³, Mohamed Mousa⁴, Anwer Mustafa Hilal^5,*, Amgad Atta Abdelmageed⁵, Abdelwahed Motwakel⁵, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3117-3131, 2023, DOI:10.32604/cmc.2023.033038

Abstract Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately… More >

Gibrael Abosamra^*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3539-3560, 2023, DOI:10.32604/cmc.2023.032976

Abstract This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or… More >

Jian Peng^1,2, Yifang Zhao^1,2, Dengyong Zhang^1,2,*, Feng Li^1,2, Arun Kumar Sangaiah³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3405-3419, 2023, DOI:10.32604/cmc.2023.027627

Abstract Recently, object detection based on convolutional neural networks (CNNs) has developed rapidly. The backbone networks for basic feature extraction are an important component of the whole detection task. Therefore, we present a new feature extraction strategy in this paper, which name is DSAFF-Net. In this strategy, we design: 1) a sandwich attention feature fusion module (SAFF module). Its purpose is to enhance the semantic information of shallow features and resolution of deep features, which is beneficial to small object detection after feature fusion. 2) to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when… More >

Dengyong Zhang^1,2, Huaijian Pu^1,2, Feng Li^1,2,*, Xiangling Ding³, Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3439-3454, 2023, DOI:10.32604/cmc.2023.027267

Abstract Now object detection based on deep learning tries different strategies. It uses fewer data training networks to achieve the effect of large dataset training. However, the existing methods usually do not achieve the balance between network parameters and training data. It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results. To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR). High-resolution feature extraction maintains the resolution representation of the image. Channels and spatial attention are… More >

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

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 positives likely to happen in… 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

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 layer, while initializing lower non-modified… 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

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 injuries in people present at… 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

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 Faster R-CNN performance was significant… 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

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 as a result, the training… 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

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 to increase the learning and… More >