He Su, Jianwei Gao, Kang Kong^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4193-4213, 2025, DOI:10.32604/cmc.2025.063355 - 19 May 2025

Abstract Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries. This study purposes a bronchoscopic lumen feature matching network (BLFM-Net) based on deep learning to address the challenges of image noise, anatomical complexity, and the stringent real-time requirements. The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules. The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing. The feature extraction module derives multi-dimensional features, such as centroids, area, and shape descriptors, from dehazed images. The Faster R-CNN Object detection module detects bronchial regions of interest and… More >

Peicheng Shi^1,*, Li Yang¹, Xinlong Dong¹, Heng Qi², Aixi Yang³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3877-3917, 2025, DOI:10.32604/cmc.2025.063205 - 19 May 2025

Abstract As the number and complexity of sensors in autonomous vehicles continue to rise, multimodal fusion-based object detection algorithms are increasingly being used to detect 3D environmental information, significantly advancing the development of perception technology in autonomous driving. To further promote the development of fusion algorithms and improve detection performance, this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms. Starting from single-modal sensor detection, the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds. For image-based detection… More >

Yung-Hsiang Hung, Chuen-Kai Fan, Wen-Pai Wang^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2323-2349, 2025, DOI:10.32604/cmc.2025.063270 - 16 April 2025

Abstract Bees play a crucial role in the global food chain, pollinating over 75% of food and producing valuable products such as bee pollen, propolis, and royal jelly. However, the Asian hornet poses a serious threat to bee populations by preying on them and disrupting agricultural ecosystems. To address this issue, this study developed a modified YOLOv7tiny (You Only Look Once) model for efficient hornet detection. The model incorporated space-to-depth (SPD) and squeeze-and-excitation (SE) attention mechanisms and involved detailed annotation of the hornet’s head and full body, significantly enhancing the detection of small objects. The Taguchi… More >

Xuejing Li^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1667-1681, 2025, DOI:10.32604/cmc.2025.062161 - 16 April 2025

Abstract Few-shot point cloud 3D object detection (FS3D) aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes. Due to imbalanced training data, existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes, which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects. To address these issues, this thesis proposes a… More >

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

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1929-1949, 2025, DOI:10.32604/cmc.2025.061363 - 16 April 2025

Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >

Min Yao^1,*, Guofeng Liu², Yaozu Zhang³, Guangjie Hu¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2189-2205, 2025, DOI:10.32604/cmc.2025.060609 - 16 April 2025

Abstract Knowledge distillation (KD) is an emerging model compression technique for learning compact object detector models. Previous KD often focused solely on distilling from the logits layer or the feature intermediate layers, which may limit the comprehensive learning of the student network. Additionally, the imbalance between the foreground and background also affects the performance of the model. To address these issues, this paper employs feature-based distillation to enhance the detection performance of the bounding box localization part, and logit-based distillation to improve the detection performance of the category prediction part. Specifically, for the intermediate layer feature… More >

Mohammad Barr^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 593-616, 2025, DOI:10.32604/cmes.2025.062264 - 11 April 2025

Abstract Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance and management. However, challenges like small object detection, scale variation, and the presence of closely packed objects in these images hinder accurate detection. Additionally, the motion blur effect further complicates the identification of such objects. To address these issues, we propose enhanced YOLOv9 with a transformer head (YOLOv9-TH). The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms. We… More >

Zhuowei Wang^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.49, pp. 377-399, 2025, DOI:10.32604/csse.2025.059634 - 09 April 2025

Abstract Synthetic Aperture Radar (SAR) has become one of the most effective tools in ship detection. However, due to significant background interference, small targets, and challenges related to target scattering intensity in SAR images, current ship target detection faces serious issues of missed detections and false positives, and the network structures are overly complex. To address this issue, this paper proposes a lightweight model based on YOLOv8, named OD-YOLOv8. Firstly, we adopt a simplified neural network architecture, VanillaNet, to replace the backbone network, significantly reducing the number of parameters and computational complexity while ensuring accuracy. Secondly,… More >

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 >