Dongdong Zhang, Chunping Wang, Huiying Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4319-4338, 2025, DOI:10.32604/cmc.2025.060653 - 06 March 2025

Abstract Video camouflaged object detection (VCOD) has become a fundamental task in computer vision that has attracted significant attention in recent years. Unlike image camouflaged object detection (ICOD), VCOD not only requires spatial cues but also needs motion cues. Thus, effectively utilizing spatiotemporal information is crucial for generating accurate segmentation results. Current VCOD methods, which typically focus on exploring motion representation, often ineffectively integrate spatial and motion features, leading to poor performance in diverse scenarios. To address these issues, we design a novel spatiotemporal network with an encoder-decoder structure. During the encoding stage, an adjacent space-time More >

Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Xinxin Liu, Bin Ye^*

Computer Systems Science and Engineering, Vol.49, pp. 287-300, 2025, DOI:10.32604/csse.2025.061655 - 20 February 2025

Abstract In the broader field of mechanical technology, and particularly in the context of self-driving vehicles, cameras and Light Detection and Ranging (LiDAR) sensors provide complementary modalities that hold significant potential for sensor fusion. However, directly merging multi-sensor data through point projection often results in information loss due to quantization, and managing the differing data formats from multiple sensors remains a persistent challenge. To address these issues, we propose a new fusion method that leverages continuous convolution, point-pooling, and a learned Multilayer Perceptron (MLP) to achieve superior detection performance. Our approach integrates the segmentation mask with… More >

Kexin Wang^1,#, Jiancheng Liu^1,#,*, Yuqing Lin^2,*, Tuo Wang¹, Zhipeng Zhang¹, Wanlong Qi¹, Xingye Han¹, Runyuan Wen³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1879-1899, 2025, DOI:10.32604/cmc.2024.058952 - 17 February 2025

Abstract Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection. Current frameworks for oriented detection modules are constrained by intrinsic limitations, including excessive computational and memory overheads, discrepancies between predefined anchors and ground truth bounding boxes, intricate training processes, and feature alignment inconsistencies. To overcome these challenges, we present ASL-OOD (Angle-based SIOU Loss for Oriented Object Detection), a novel, efficient, and robust one-stage framework tailored for oriented object detection. The ASL-OOD framework comprises three core components: the Transformer-based Backbone (TB), the Transformer-based Neck… More >

Honglin Wang¹, Yangyang Zhang^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3399-3417, 2025, DOI:10.32604/cmc.2024.058932 - 17 February 2025

Abstract Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction… More >

Honglin Wang¹, Zitong Shi^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2451-2474, 2025, DOI:10.32604/cmc.2024.058474 - 17 February 2025

Abstract In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different… More >

Libo Han^1,2, Sha Tao^1,2, Wen Xia³, Weixin Sun³, Li Yan³, Wanlin Gao^1,2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2431-2449, 2025, DOI:10.32604/cmc.2024.057833 - 17 February 2025

Abstract At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature… More >

Xunguang Yan^1,2, Wenrui Wang¹, Fanglin Lu¹, Hongyong Fan³, Bo Wu¹, Jianfeng Yu^1,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1439-1458, 2025, DOI:10.32604/cmc.2024.057797 - 03 January 2025

Abstract To maintain the reliability of power systems, routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues. The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods, especially in identifying small objects in high-resolution images. This study presents an enhanced object detection algorithm based on the Faster Region-based Convolutional Neural Network (Faster R-CNN) framework, specifically tailored for detecting small-scale electrical components like insulators, shock hammers, and screws in transmission line. The algorithm features an improved backbone network for Faster R-CNN, which significantly boosts the More >

Menglin Yin¹, Yong Qin^1,2,3,4,*, Jiansheng Peng^1,2,3,4

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1329-1347, 2025, DOI:10.32604/cmc.2024.057460 - 03 January 2025

Abstract In dynamic scenarios, visual simultaneous localization and mapping (SLAM) algorithms often incorrectly incorporate dynamic points during camera pose computation, leading to reduced accuracy and robustness. This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability. Firstly, a parallel thread employs the YOLOX object detection model to gather 2D semantic information and compensate for missed detections. Next, an improved K-means++ clustering algorithm clusters bounding box regions, adaptively determining the threshold for extracting dynamic object contours as dynamic points change. This process divides the image into low dynamic, suspicious dynamic, and high More >

Diana Abi-Nader¹, Hassan Harb², Ali Jaber¹, Ali Mansour³, Christophe Osswald³, Nour Mostafa^2,*, Chamseddine Zaki²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 279-298, 2025, DOI:10.32604/cmes.2024.056816 - 17 December 2024

Abstract Security and safety remain paramount concerns for both governments and individuals worldwide. In today’s context, the frequency of crimes and terrorist attacks is alarmingly increasing, becoming increasingly intolerable to society. Consequently, there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces, thereby preventing potential attacks or violent incidents. Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection, particularly in identifying firearms. This paper introduces a novel automatic firearm detection surveillance system, utilizing a one-stage detection… More >