Conghao Niu^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

Trong Thua Huynh^*, Hoang Thanh Nguyen, Du Thang Phu

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024

Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

Yuefan Liu, Ducheng Zhang, Chen Guo^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3281-3299, 2024, DOI:10.32604/cmc.2024.057294 - 18 November 2024

Abstract Craniocerebral injuries represent the primary cause of fatalities among riders involved in two-wheeler accidents; nevertheless, the prevalence of helmet usage among these riders remains alarmingly low. Consequently, the accurate identification of riders who are wearing safety helmets is of paramount importance. Current detection algorithms exhibit several limitations, including inadequate accuracy, substantial model size, and suboptimal performance in complex environments with small targets. To address these challenges, we propose a novel lightweight detection algorithm, termed GL-YOLOv5, which is an enhancement of the You Only Look Once version 5 (YOLOv5) framework. This model incorporates a Global DualPooling… More >

Keke Zhou, Guoqiang Zheng^*, Huihui Zhai, Xiangshuai Lv, Weizhen Zhang

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2571-2585, 2024, DOI:10.32604/cmc.2024.056863 - 18 November 2024

Abstract Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies. Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents, thereby providing a guarantee for the safety of drivers. However, detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds, varying target scales, and different resolutions. Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios, this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5. The algorithm integrates Attention-based Intra-scale Feature… More >

Zongdong Du^1,2, Xuefeng Feng³, Feng Li³, Qinglong Xian³, Zhenhong Jia^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2607-2627, 2024, DOI:10.32604/cmc.2024.056616 - 18 November 2024

Abstract The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable… More >

Chenghai Yu, Zhilong Lu^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3261-3280, 2024, DOI:10.32604/cmc.2024.056413 - 18 November 2024

Abstract Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite advances in defect detection technologies, research specifically targeting railway turnout defects remains limited. To address this gap, we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments. To enhance detection accuracy, we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU (YOLO-VSI). The model employs a state-space model (SSM) to enhance the C2f module in the YOLOv8… More >

Bing Shi^1,*, Jianhua Zhao¹, Bin Ma¹, Juan Huan², Yueping Sun³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2437-2456, 2024, DOI:10.32604/cmc.2024.056377 - 18 November 2024

Abstract Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture. Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses. To address this issue, an improved algorithm based on the You Only Look Once v5s (YOLOv5s) lightweight model has been proposed. This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module (CBAM) to achieve high recognition accuracy. Furthermore, the model introduces the α-SIoU loss function, which combines the α-Intersection over Union (α-IoU) and… More >

Mingyuan Zhai^1,2, Hanquan Zhang¹, Le Wang¹, Dong Xiao^1,*, Zhengmin Gu³, Zhenni Li¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3241-3260, 2024, DOI:10.32604/cmc.2024.056241 - 18 November 2024

Abstract In the process of dense vehicles traveling fast, there will be mutual occlusion between vehicles, which will lead to the problem of deterioration of the tracking effect of different vehicles, so this paper proposes a research method of virtual simulation video vehicle target tracking based on you only look once (YOLO)v5s and deep simple online and realtime tracking (DeepSort). Given that the DeepSort algorithm is currently the most effective tracking method, this paper merges the YOLOv5 algorithm with the DeepSort algorithm. Then it adds the efficient channel attention networks (ECA-Net) focusing mechanism at the back… More >

Mehwash Farooqui¹, Atta Rahman^2,*, Latifa Alsuliman¹, Zainab Alsaif¹, Fatimah Albaik¹, Cadi Alshammari¹, Razan Sharaf¹, Sunday Olatunji¹, Sara Waslallah Althubaiti¹, Hina Gull³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2587-2605, 2024, DOI:10.32604/cmc.2024.055262 - 18 November 2024

Abstract The proposed study focuses on the critical issue of corrosion, which leads to significant economic losses and safety risks worldwide. A key area of emphasis is the accuracy of corrosion detection methods. While recent studies have made progress, a common challenge is the low accuracy of existing detection models. These models often struggle to reliably identify corrosion tendencies, which are crucial for minimizing industrial risks and optimizing resource use. The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network (CNN), as well as two pretrained… More >

Jiajun Liu¹, Lina Tan^1,*, Zhili Zhou², Weijin Jiang¹, Yi Li¹, Peng Chen¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3221-3240, 2024, DOI:10.32604/cmc.2024.054542 - 18 November 2024

Abstract Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data. One issue with these methods is that as the steganographic capacity increases, the number of images stored in the database grows exponentially. This makes it challenging to build and manage a large image database. To improve the image library utilization and anti-attack capability of the steganography system, we propose an efficient coverless scheme based on dynamically matched substrings. We utilize You Only Look Once (YOLO) for selecting optimal objects and create a mapping dictionary between these objects and scrambling factors.… More >