Manar Ahmed Hamza^1,*, Hanan Abdullah Mengash², Noha Negm³, Radwa Marzouk², Abdelwahed Motwakel¹, Abu Sarwar Zamani¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4191-4205, 2023, DOI:10.32604/cmc.2023.027050 - 31 March 2023

Abstract Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in… More >

Onder Alparslan^*, Omer Cetin

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3355-3370, 2023, DOI:10.32604/iasc.2023.035689 - 15 March 2023

Abstract Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area. One of the simplest and most efficient algorithms, the artificial potential field algorithm (APF), may provide real-time navigation in those places but fall into local minimum in some cases. To overcome this problem and to present alternative escape routes for a robot, possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm. This study utilized a proposed sensor fusion method and an… More >

Yifeng Liu¹, Yangyang Li^1,*, Zhongxiong Xu¹, Xiaohan Liu¹, Haiyong Xie², Huacheng Zeng³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2519-2528, 2023, DOI:10.32604/iasc.2023.033286 - 15 March 2023

Abstract Deep convolution neural networks are going deeper and deeper. However, the complexity of models is prone to overfitting in training. Dropout, one of the crucial tricks, prevents units from co-adapting too much by randomly dropping neurons during training. It effectively improves the performance of deep networks but ignores the importance of the differences between neurons. To optimize this issue, this paper presents a new dropout method called guided dropout, which selects the neurons to switch off according to the differences between the convolution kernel and preserves the informative neurons. It uses an unsupervised clustering algorithm… More >

B. Sophia^1,*, D. Chitra²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2491-2504, 2023, DOI:10.32604/iasc.2023.029799 - 15 March 2023

Abstract Presently, video surveillance is commonly employed to ensure security in public places such as traffic signals, malls, railway stations, etc. A major challenge in video surveillance is the identification of anomalies that exist in it such as crimes, thefts, and so on. Besides, the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety. The recent advances of Deep Learning (DL) models have received considerable attention in different processes such as object detection, image classification, etc. In this aspect, this article designs a new Panoptic Feature Pyramid… More >

G. Augusta Kani^*, P. Geetha

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1447-1469, 2023, DOI:10.32604/csse.2023.035969 - 09 February 2023

Abstract Human Activity Recognition (HAR) from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications, video-based behavior monitoring, video classification, and indexing, patient monitoring, robotics, and behavior analysis. Although many techniques are available for HAR in video analysis tasks, most of them are not focusing on behavioral analysis. Hence, a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work. The most essential aim of this work is to recognize the complex activities that are… More >

Zihao Sun^1,*, Xiao Hu², Yining Qi², Yongfeng Huang², Songbin Li³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1657-1669, 2023, DOI:10.32604/cmc.2023.036558 - 06 February 2023

Abstract The marine environment is becoming increasingly complex due to the various marine vehicles, and the diversity of maritime objects poses a challenge to marine environmental governance. Maritime object detection technology plays an important role in this segment. In the field of computer vision, there is no sufficiently comprehensive public dataset for maritime objects in the contrast to the automotive application domain. The existing maritime datasets either have no bounding boxes (which are made for object classification) or cover limited varieties of maritime objects. To fulfil the vacancy, this paper proposed the Multi-Category Large-Scale Dataset for… More >

Geng Qin¹, Ping Luo¹, Kaiyuan Li¹, Yufeng Sun¹, Shiwei Wang¹, Xiaoting Li^1,2,3, Shuang Liu^1,2,3, Linyan Xue^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 999-1015, 2023, DOI:10.32604/cmc.2023.035777 - 06 February 2023

Abstract Due to small size and high occult, metacarpophalangeal fracture diagnosis displays a low accuracy in terms of fracture detection and location in X-ray images. To efficiently detect metacarpophalangeal fractures on X-ray images as the second opinion for radiologists, we proposed a novel one-stage neural network named MPFracNet based on RetinaNet. In MPFracNet, a deformable bottleneck block (DBB) was integrated into the bottleneck to better adapt to the geometric variation of the fractures. Furthermore, an integrated feature fusion module (IFFM) was employed to obtain more in-depth semantic and shallow detail features. Specifically, Focal Loss and Balanced… More >

Siddiqui Muhammad Yasir¹, Hyunsik Ahn^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1847-1861, 2023, DOI:10.32604/cmc.2023.035698 - 06 February 2023

Abstract Deep learning has been constantly improving in recent years, and a significant number of researchers have devoted themselves to the research of defect detection algorithms. Detection and recognition of small and complex targets is still a problem that needs to be solved. The authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel surfaces. During steel strip production, mechanical forces and environmental factors cause surface defects of the steel strip. Therefore, the detection of such defects is key to the production of high-quality… More >

M. Zeeshan Sarwar¹, Mohammed Hamad Alatiyyah², Ahmad Jalal¹, Mohammad Shorfuzzaman³, Nawal Alsufyani³, Jeongmin Park^4,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 959-976, 2023, DOI:10.32604/cmc.2023.035442 - 06 February 2023

Abstract In the last decade, there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras. They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios. Depth cues can improve the quality of object detection and recognition. The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets. The proposed methodology extracts image normally… More >

Xinliang Tang¹, Caixing Wang¹, Jingfang Su^1,*, Cecilia Taylor²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 117-131, 2023, DOI:10.32604/cmc.2023.033327 - 06 February 2023

Abstract Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used… More >