Sixian Chan^1,2, Binghui Wu¹, Guodao Zhang³, Yuan Yao⁴, Hongqiang Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485 - 22 September 2023

Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection… More > Graphic Abstract

Chao Dong, Xiangkui Jiang^*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3015-3036, 2023, DOI:10.32604/cmc.2023.044735 - 26 December 2023

Abstract To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images, this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds, called DI-YOLO, based on You Only Look Once v7-tiny (YOLOv7-tiny). Firstly, to enhance the model’s ability to capture irregular-shaped objects and deformation features, as well as to extract high-level semantic information, deformable convolutions are used to replace standard convolutions in the original model. Secondly, a Content Coordination Attention Feature Pyramid Network (CCA-FPN) structure is designed to replace the… More >

Ananthakrishnan Balasundaram^1,*, Anshuman Mohanty², Ayesha Shaik¹, Krishnadoss Pradeep², Kedalu Poornachary Vijayakumar², Muthu Subash Kavitha³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2751-2769, 2023, DOI:10.32604/cmc.2023.044374 - 26 December 2023

Abstract Automated object detection has received the most attention over the years. Use cases ranging from autonomous driving applications to military surveillance systems, require robust detection of objects in different illumination conditions. State-of-the-art object detectors tend to fare well in object detection during daytime conditions. However, their performance is severely hampered in night light conditions due to poor illumination. To address this challenge, the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime conditions. Firstly, the preprocessing strategies involve using the Zero-DCE++ approach to enhance lowlight images. It is followed… More >

Ye-Yeon Kang¹, Geon Park¹, Hyun Yoo², Kyungyong Chung^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3619-3635, 2023, DOI:10.32604/cmc.2023.043566 - 26 December 2023

Abstract Object tracking, an important technology in the field of image processing and computer vision, is used to continuously track a specific object or person in an image. This technology may be effective in identifying the same person within one image, but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same. When tracking the same object using two or more images, there must be a way to determine that objects existing in different images are the same object. Therefore, this paper attempts… More >

Jiansheng Peng^1,2,*, Dunhua Chen¹, Qing Yang¹, Chengjun Yang², Yong Xu², Yong Qin²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3209-3236, 2023, DOI:10.32604/cmc.2023.041898 - 26 December 2023

Abstract Visual simultaneous localization and mapping (SLAM) is crucial in robotics and autonomous driving. However, traditional visual SLAM faces challenges in dynamic environments. To address this issue, researchers have proposed semantic SLAM, which combines object detection, semantic segmentation, instance segmentation, and visual SLAM. Despite the growing body of literature on semantic SLAM, there is currently a lack of comprehensive research on the integration of object detection and visual SLAM. Therefore, this study aims to gather information from multiple databases and review relevant literature using specific keywords. It focuses on visual SLAM based on object detection, covering… More >

Jian Wei, Qinzhao Wang^*, Zixu Zhao

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1699-1717, 2023, DOI:10.32604/cmc.2023.044284 - 29 November 2023

Abstract The detection of large-scale objects has achieved high accuracy, but due to the low peak signal to noise ratio (PSNR), fewer distinguishing features, and ease of being occluded by the surroundings, the detection of small objects, however, does not enjoy similar success. Endeavor to solve the problem, this paper proposes an attention mechanism based on cross-Key values. Based on the traditional transformer, this paper first improves the feature processing with the convolution module, effectively maintaining the local semantic context in the middle layer, and significantly reducing the number of parameters of the model. Then, to More >

Wei Wang¹, Xinyao Tang^2,*, Kai Zhou¹, Chunhui Zhao¹, Changfa Liu³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1911-1928, 2023, DOI:10.32604/cmc.2023.043233 - 29 November 2023

Abstract Data Matrix (DM) codes have been widely used in industrial production. The reading of DM code usually includes positioning and decoding. Accurate positioning is a prerequisite for successful decoding. Traditional image processing methods have poor adaptability to pollution and complex backgrounds. Although deep learning-based methods can automatically extract features, the bounding boxes cannot entirely fit the contour of the code. Further image processing methods are required for precise positioning, which will reduce efficiency. Because of the above problems, a CenterNet-based DM code key point detection network is proposed, which can directly obtain the four key… More >

Yujun Zhang^*, Dezhi Han, Peng Chen

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311 - 29 November 2023

Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information… More >

Ehab Bahaudien Ashary¹, Sahar Jambi², Rehab B. Ashari², Mahmoud Ragab^3,4,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2953-2969, 2023, DOI:10.32604/csse.2023.041523 - 09 November 2023

Abstract Object Detection is the task of localization and classification of objects in a video or image. In recent times, because of its widespread applications, it has obtained more importance. In the modern world, waste pollution is one significant environmental problem. The prominence of recycling is known very well for both ecological and economic reasons, and the industry needs higher efficiency. Waste object detection utilizing deep learning (DL) involves training a machine-learning method to classify and detect various types of waste in videos or images. This technology is utilized for several purposes recycling and sorting waste,… More >

P. Kuppusamy^1,*, M. Sanjay¹, P. V. Deepashree¹, C. Iwendi²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 445-466, 2023, DOI:10.32604/cmc.2023.042675 - 31 October 2023

Abstract The infrastructure and construction of roads are crucial for the economic and social development of a region, but traffic-related challenges like accidents and congestion persist. Artificial Intelligence (AI) and Machine Learning (ML) have been used in road infrastructure and construction, particularly with the Internet of Things (IoT) devices. Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing traffic-related problems. This study aims to use You Only Look Once version 7 (YOLOv7), Convolutional Block Attention Module (CBAM), the most optimized object-detection algorithm, to detect and identify traffic signs, and… More >