Zhe Chen^*, Yinyang Zhang, Sihao Xing

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1787-1803, 2025, DOI:10.32604/cmc.2025.065238 - 09 June 2025

Abstract Unmanned aerial vehicle (UAV) imagery poses significant challenges for object detection due to extreme scale variations, high-density small targets (68% in VisDrone dataset), and complex backgrounds. While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion, their rigid architectures struggle with multi-scale adaptability, as exemplified by YOLOv8n’s 36.4% mAP and 13.9% small-object AP on VisDrone2019. This paper presents YOLO-LE, a lightweight framework addressing these limitations through three novel designs: (1) We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters, thereby improving More >

Yu-Xiang Zhao^*, Yi Lee

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 791-810, 2025, DOI:10.32604/cmc.2025.065048 - 09 June 2025

Abstract Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research. Additionally, performing edge computing on low-level devices using small neural networks can be an important research direction. In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the original training set as a form of data augmentation.… More >

Maazen Alsabaan¹, Zaid Bin Faheem², Yuanyuan Zhu², Jehad Ali^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 491-512, 2025, DOI:10.32604/cmc.2025.064971 - 09 June 2025

Abstract Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms. Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content. Image watermarking can also be used to verify the authenticity of digital media, such as images or videos, by ascertaining the watermark information. In this paper, a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform (DWT), chaotic map, and Laplacian operator. The DWT can be used to decompose… More >

Haoyang Tang^1,2, Kai Zeng^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1485-1506, 2025, DOI:10.32604/cmc.2025.064441 - 09 June 2025

Abstract Semantic segmentation provides important technical support for Land cover/land use (LCLU) research. By calculating the cosine similarity between feature vectors, transformer-based models can effectively capture the global information of high-resolution remote sensing images. However, the diversity of detailed and edge features within the same class of ground objects in high-resolution remote sensing images leads to a dispersed embedding distribution. The dispersed feature distribution enlarges feature vector angles and reduces cosine similarity, weakening the attention mechanism’s ability to identify the same class of ground objects. To address this challenge, remote sensing image information granulation transformer for… More >

Thanh Bui-Minh¹, Nguyen Long Giang¹, Luan Thanh Le^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1203-1226, 2025, DOI:10.32604/cmc.2025.064403 - 09 June 2025

Abstract Image-based similar trademark retrieval is a time-consuming and labor-intensive task in the trademark examination process. This paper aims to support trademark examiners by training Deep Convolutional Neural Network (DCNN) models for effective Trademark Image Retrieval (TIR). To achieve this goal, we first develop a novel labeling method that automatically generates hundreds of thousands of labeled similar and dissimilar trademark image pairs using accompanying data fields such as citation lists, Vienna classification (VC) codes, and trademark ownership information. This approach eliminates the need for manual labeling and provides a large-scale dataset suitable for training deep learning… More >

Kangkang Zhao¹, Yaojie Chen^1,*, Jianbo Li²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 745-762, 2025, DOI:10.32604/cmc.2025.063906 - 09 June 2025

Abstract Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects. Event cameras, as high temporal resolution bionic cameras, record intensity changes in an asynchronous manner, and their recorded high temporal resolution information can effectively solve the problem of time information loss in motion blur. Existing event-based deblurring methods still face challenges when facing high-speed moving objects. We conducted an in-depth study of the imaging principle of event cameras. We found that the event stream contains excessive noise. The valid information is sparse. Invalid event features hinder the expression of valid features due to… More >

Atheer Alrammahi^1,2, Hedieh Sajedi^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 887-918, 2025, DOI:10.32604/cmc.2025.063866 - 09 June 2025

Abstract Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution, tampering, and copyright infringement. This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform (DWT), Redundant Discrete Wavelet Transform (RDWT), and Möbius Transformations (MT), with optimization of transformation parameters achieved via a Genetic Algorithm (GA). By combining frequency and spatial domain techniques, the proposed method significantly enhances both the imperceptibility and robustness of watermark embedding. The approach leverages DWT and RDWT for multi-resolution decomposition, enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks. RDWT,… More >

Kezhen Xie^1,2, Syed Mohd Zahid Syed Zainal Ariffin^1,*, Muhammad Izzad Ramli¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 997-1011, 2025, DOI:10.32604/cmc.2025.063469 - 09 June 2025

Abstract Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images. However, existing methods often fail to distinguish salient objects from background regions, leading to detail suppression in salient regions due to global fusion strategies. This study presents a mask-guided latent low-rank representation fusion method to address this issue. First, the GrabCut algorithm is employed to extract a saliency mask, distinguishing salient regions from background regions. Then, latent low-rank representation (LatLRR) is applied to extract deep image features, enhancing More >

Ghada Atteia^1,*, Mohammed Dabboor², Konstantinos Karantzalos³, Maali Alabdulhafith¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

Qing Xie^*, Ruiyun Yu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1123-1144, 2025, DOI:10.32604/cmc.2025.062712 - 09 June 2025

Abstract Although existing style transfer techniques have made significant progress in the field of image generation, there are still some challenges in the field of exhibition hall design. The existing style transfer methods mainly focus on the transformation of single dimensional features, but ignore the deep integration of content and style features in exhibition hall design. In addition, existing methods are deficient in detail retention, especially in accurately capturing and reproducing local textures and details while preserving the content image structure. In addition, point-based attention mechanisms tend to ignore the complexity and diversity of image features… More >