Dike Chen^1,2,3, Zhiyong Qin², Ji Zhang², Hongyuan Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.072494 - 09 December 2025

Abstract To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle (UAV) aerial imagery, which often lead to missed and false detections, we propose Multi-scale Feature Fusion YOLO (MFF-YOLO), an enhanced algorithm based on YOLOv8s. Our approach introduces a Multi-scale Feature Fusion Strategy (MFFS), comprising the Multiple Features C2f (MFC) module and the Scale Sequence Feature Fusion (SSFF) module, to improve feature integration across different network levels. This enables more effective capture of fine-grained details and sequential multi-scale features. Furthermore, we incorporate Inner-CIoU, an improved loss function that uses auxiliary More >

Jianhua Yang^*, Zhiwei Lv, Changling Huo

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068442 - 10 November 2025

Abstract Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing, this paper proposes a YOLO-LKSDS automatic driving detection model. Firstly, the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target; then, on the basis of the YOLOv5 model, the Kmeans++ clustering algorithm is introduced to obtain a suitable anchor frame, and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target… More >

Miao Li^1,2,*, Xiaojun Li^1,3, Mingyang Zhao^1,2

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1917-1936, 2025, DOI:10.32604/cmc.2025.067382 - 29 August 2025

Abstract Aiming at problems such as large errors and low efficiency in manual counting of drill pipes during drilling depth measurement, an intelligent detection and counting method for the small targets at the end of drill pipes based on the improved YOLO11n is proposed. This method realizes the high-precision detection of targets at drill pipe ends in the image by optimizing the target detection model, and combines a post-processing correction mechanism to improve the drill pipe counting accuracy. In order to alleviate the low-precision problem of YOLO11n algorithm for small target recognition in the complex underground… More >

Haiyan Chen^*, Rong Li

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5693-5712, 2025, DOI:10.32604/cmc.2025.065339 - 30 July 2025

Abstract The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations. In natural environments, the pixels representing Ochotona Curzoniae constitute a small fraction of the total pixels, and their distinguishing features are often subtle, complicating the target detection process. To effectively extract the characteristics of these small targets, a feature fusion approach that utilizes up-sampling and channel integration from various layers within a CNN can significantly enhance the representation of target features, ultimately improving detection accuracy. However, the top-down… More >

Siqi Zhang, Shengda Pan^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4655-4676, 2025, DOI:10.32604/cmc.2025.064864 - 30 July 2025

Abstract Infrared images typically exhibit diverse backgrounds, each potentially containing noise and target-like interference elements. In complex backgrounds, infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features, leading to detection failure. To address this problem, this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network (ASCFNet) tailored for the detection of infrared weak and small targets. The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module (MLPA), which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping,… More >

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 >

Jianyin Tang, Zhenglin Yu^*, Changshun Shao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2829-2850, 2025, DOI:10.32604/cmc.2025.063287 - 16 April 2025

Abstract In the field of automated fruit harvesting, precise and efficient fruit target recognition and localization play a pivotal role in enhancing the efficiency of harvesting robots. However, this domain faces two core challenges: firstly, the dynamic nature of the automatic picking process requires fruit target detection algorithms to adapt to multi-view characteristics, ensuring effective recognition of the same fruit from different perspectives. Secondly, fruits in natural environments often suffer from interference factors such as overlapping, occlusion, and illumination fluctuations, which increase the difficulty of image capture and recognition. To address these challenges, this study conducted… More >

Lijuan Zhang^1,2, Xiaoyu Wang^1,2, Songtao Zhang³, Yutong Jiang^4,*, Dongming Li¹, Weichen Sun⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1219-1237, 2025, DOI:10.32604/cmc.2025.059856 - 26 March 2025

Abstract In this paper, a reasoning enhancement method based on RGCN (Relational Graph Convolutional Network) is proposed to improve the detection capability of UAV (Unmanned Aerial Vehicle) on fast-moving military targets in urban battlefield environments. By combining military images with the publicly available VisDrone2019 dataset, a new dataset called VisMilitary was built and multiple YOLO (You Only Look Once) models were tested on it. Due to the low confidence problem caused by fuzzy targets, the performance of traditional YOLO models on real battlefield images decreases significantly. Therefore, we propose an improved RGCN inference model, which improves More >

Pengcheng Gao^*, Zhenjiang Li

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4555-4572, 2025, DOI:10.32604/cmc.2025.060873 - 06 March 2025

Abstract The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of More >

Zhenyang He, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4301-4317, 2025, DOI:10.32604/cmc.2025.060550 - 06 March 2025

Abstract Tomato plant diseases often first manifest on the leaves, making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry. However, conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery. This paper proposes a lightweight model for detecting tomato leaf diseases, named LT-YOLO, based on the YOLOv8n architecture. First, we enhance the C2f module into a RepViT Block (RVB) with decoupled token and channel mixers to reduce the cost of feature extraction. Next, we incorporate a novel Efficient… More >