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This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. The model was trained on 70,389 samples across 20 target categories. After 496 training epochs, the loss functions (Box_Loss, Cls_Loss, and DFL_Loss) demonstrated rapid convergence, indicating effective optimization in target localization, classification, and detail refinement. The evaluation metrics yielded a precision of 0.8861, a recall of 0.8563, a map 50 of 0.8920, a map 50–95 of 0.8646, and an F1 score of 0.8709, highlighting the model’s high accuracy and robustness in addressing complex detection tasks. Furthermore, 80% of the test samples achieved confidence scores exceeding 85%, confirming the reliability of YOLOv11 in multiclass and multiobject detection scenarios. These findings suggest that YOLOv11 holds significant promise for remote sensing image target detection, demonstrating exceptional detection performance while offering robust technical support for intelligent remote sensing image analysis. Future studies will focus on expanding the dataset, refining the model architecture, and improving its performance in detecting small targets and processing complex scenes, paving the way for its broader applications in environmental protection, urban planning, and multiobject detection.

IntroductionTimely identification and precise segmentation of tea leaf diseases are essential for intelligent agricultural management. However, balancing lightweight deployment and high-precision segmentation remains challenging under uneven illumination, background interference, and subtle early-stage lesion textures in natural environments.MethodsWe propose TDS-YOLO, a lightweight segmentation model based on the YOLOv11 framework. The model introduces three innovations: (1) C3K2_EViM_CGLU for global dependency modeling, (2) EfficientHead for lightweight pixel-level representation, and (3) C2PSA_Mona to enhance multi-scale texture perception.ResultsExperiments on a diverse dataset of 4,933 images show that TDS-YOLO achieves state-of-the-art performance with only 2.53M parameters. It reaches an mAP@0.5 of 90.1% for both detection and segmentation, outperforming YOLOv11-seg and other mainstream models while maintaining an inference speed of 96 FPS.DiscussionThe proposed approach provides an efficient and robust solution for real-time monitoring of tea diseases, supporting precision tea plantation management and broader smart digital agriculture applications.

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. The review traces the evolution of YOLO variants, highlighting key architectural improvements, performance benchmarks, and applications in domains such as healthcare, autonomous vehicles, and robotics. It also evaluates the framework’s strengths and limitations in practical scenarios, addressing challenges like small object detection, environmental variability, and computational constraints. By synthesizing findings from recent research, this work identifies critical gaps in the literature and outlines future directions to enhance YOLO’s adaptability, robustness, and integration into emerging technologies. This review provides researchers and practitioners with valuable insights to drive innovation in object detection and related applications.

Flower thinning plays a vital role in peach production, which significantly affects fruit yield and quality. Obtaining precise information about inflorescences is the key to scientific thinning and refined orchard management. However, the accurate detection of peach inflorescence still faces great challenges due to the complex and changeable light conditions, dense occlusion between flowers and significant scale differences in the actual orchard environment. In order to solve these problems, an enhanced YOLOv11s peach inflorescence detection model, termed MDI-YOLOv11, is proposed in this study to achieve accurate and stable recognition of flowers and buds. Considering the characteristics of small target and frequent occlusion in peach inflorescences, a collaborative design of the neck feature fusion structure and the backbone feature attention mechanism is adopted. Specifically, the RFCAConv module is added to the backbone network to increase sensitivity to salient regions, while a P2 layer for small target detection is embedded within the neck network and integrated with the RepGFPN structure to enhance multi-scale feature fusion, thereby improving detection accuracy and adaptability in complex orchard environments. The model’s performance was systematically assessed on a self-built dataset comprising 1,008 images. The dataset labeled 41,962 target instances after sample balancing, including 22,803 flower targets and 19,159 bud targets, covering typical orchard scenes with varying illumination, color characteristics, and high density occlusion. The five-fold cross-validation experiment demonstrated that MDI-YOLOv11 achieved an AP 50 of 0.919 and an AR 50 of 0.964 for peach tree inflorescences detection, along with a detection time of 13.46 ms per image. 10.97 million parameters, and a model size of 21.51MB, all of which meet practical application requirements. Compared with the YOLOv11s model, the MDI-YOLOv11 model achieved a 0.033 increase in both AP 50 and AR 50 , and the detection performance and model complexity are better than YOLOv11m. Based on the detection results of MDI-YOLOv11, this study generated row-by-row inflorescence density distribution maps that intuitively displayed the spatial density distribution of peach inflorescences. The results indicate that the proposed method enables efficient and accurate detection of peach flowers and the generation of inflorescence density maps, which is expected to provide effective support for refined orchards management.

The content of impurities in mechanically harvested sugarcane is a critical factor for evaluating harvest quality and determining market price. To enable intelligent detection of impurities in mechanically harvested sugarcane, this study proposes an impurity detection method based on an improved YOLOv11n-seg model. The method integrates four enhancement modules into the original YOLOv11n-seg architecture. Firstly, a lightweight C2_Ghost module is introduced into the high-channel feature extraction stages of both the backbone and neck, thereby reducing computational complexity and feature redundancy. Subsequently, a C2_FSAS module is designed to perform frequency-domain relationship modelling, enhancing long-range semantic dependency representation. An Efficient Channel Attention (ECA) mechanism is then applied to deep high-level semantic features to adaptively reweight salient feature channels. Finally, the traditional fixed interpolation-based upsampling operation is replaced with a dynamic DySample upsampling strategy to recover fine-grained edge features. Experimental results indicate that Improved YOLOv11n-seg achieves segmentation performance of 97.0%, 98.1%, 99.2%, and 82.9% in terms of P, R, mAP 0.5 , and mAP 0.5:0.95 , respectively. Compared with the original YOLOv11n-seg, the proposed model achieves a 1.8% improvement in mAP0.5:0.95, a 10.2% reduction in parameter count, and maintains a real-time inference speed of 34.8 FPS on the Jetson Xavier NX under TensorRT acceleration. Ablation studies validate the effectiveness of the four-module synergistic design, with C2_FSAS and DySample contributing most significantly to the improvement in mAP. Moreover, the model exhibits enhanced edge delineation accuracy and inter-class discrimination capability. In summary, the Improved YOLOv11n-seg achieves a favourable balance between segmentation accuracy and real-time performance, enabling precise segmentation of sugarcane segments and diverse impurity types. The proposed method provides reliable technical support for intelligent impurity rate detection in mechanically harvested sugarcane and practical deployment on edge computing platforms.

Detecting and segmenting damaged wires in substations is challenging due to varying lighting conditions and limited annotated data, which degrade model accuracy and robustness. In this paper, a novel 24 h × 7 days broken wire detection and segmentation framework based on dynamic multi-window attention and meta-transfer learning is proposed, comprising a low-light image enhancement module, an improved detection and segmentation network with dynamic multi-scale window attention (DMWA) based on YOLOv11n, and a multi-stage meta-transfer learning strategy to support small-sample training while mitigating negative transfer. An RGB dataset of 3760 images is constructed, and performance is evaluated under six lighting conditions ranging from 10 to 200,000 lux. Experimental results demonstrate that the proposed framework markedly improves detection and segmentation performance, as well as robustness across varying lighting conditions.

To address the challenges of underwater target detection, including complex background interference, light attenuation, severe occlusion, and overlap between targets, as well as the wide-scale variation in objects, we propose MAS-YOLOv11, an improved model integrating three key enhancements: First, we introduce the C2PSA_MSDA module, which integrates multi-scale dilated attention (MSDA) into the C2PSA module of the backbone, enhancing multi-scale feature representation via dilated convolutions and cross-scale attention. Second, an adaptive spatial feature fusion detection head (ASFFHead) replaces the original head. By employing learnable spatial weighting parameters, ASFFHead adaptively fuses features across different scales, significantly improving the robustness of multi-scale object detection. Third, we introduce a Slide Loss function with dynamic sample weighting to enhance hard sample learning. By mapping the loss weights nonlinearly to detection confidence, this mechanism effectively enhances the overall detection accuracy. The experimental results demonstrate that the improved model yields significant performance advancements on the DUO dataset: the recall rate is enhanced by 3.7%, the F1-score is elevated by 3%, and the mAP@50 and mAP@50-95 attain values of 77.4% and 55.1%, respectively, representing increases of 3.5% and 3.3% compared to the baseline model. Furthermore, the model achieves an mAP@50 of 76% on the RUOD dataset, which further corroborates its cross-domain generalization capability.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide, affecting over 55 million people globally, with numbers expected to rise dramatically. Early detection and classification of AD are crucial for improving patient outcomes and slowing disease progression. However, conventional diagnostic approaches often fail to provide accurate classification in the early stages. This paper proposes a novel approach using advanced computer-aided diagnostic (CAD) systems and the YOLOv11 neural network for early detection and classification of AD. The YOLOv11 model leverages its advanced object detection capabilities to simultaneously localize and classify AD-related biomarkers by integrating multimodal data fusion of T2-weighted MRI and DTI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Regions of interest (ROIs) were selected and annotated based on known AD biomarkers, and the YOLOv11 model was trained to classify AD into four stages: Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Mild Cognitive Impairment (MCI). The model achieved exceptional performance, with 93.6% precision, 91.6% recall, and 96.7% mAP50, demonstrating its ability to identify subtle biomarkers by combining MRI and DTI modalities. This work highlights the novelty of using YOLOv11 for simultaneous detection and classification, offering a promising strategy for early-stage AD diagnosis and classification.

In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management. This article presents an enhanced algorithm based on YOLOv11n, referred to as GCB-YOLOv11. The proposed improvements are realized through three key aspects: Firstly, the traditional convolution is replaced with GSConv, which significantly enhances feature extraction capabilities while simultaneously reducing computational costs. Secondly, a novel C3K2_FE module was designed that integrates Faster_block and ECA attention mechanisms. This design aims to improve detection accuracy while also accelerating detection speed. Finally, the introduction of the Bi FPN mechanism in the Neck section optimizes the efficiency of multi-scale feature fusion and addresses issues related to feature loss and redundancy. The experimental results demonstrate that GCB-YOLOv11 exhibits strong performance on the dataset concerning mine personnel and safety helmets, achieving a mean average precision of 93.6%. Additionally, the frames per second reached 90.3 f·s−1, representing increases of 3.3% and 9.4%, respectively, compared to the baseline model. In addition, when compared to models such as YOLOv5s, YOLOv8s, YOLOv3 Tiny, Fast R-CNN, and RT-DETR, GCB-YOLOv11 demonstrates superior performance in both detection accuracy and model complexity. This highlights its advantages in mining environments and offers a viable technical solution for enhancing the safety of mine personnel.

To address the limitations of unimodal visual detection in complex scenarios involving low illumination, occlusion, and texture-sparse environments, this paper proposes an improved YOLOv11-based dual-branch RGB-D fusion framework. The symmetric architecture processes RGB images and depth maps in parallel, integrating a Dual-Encoder Cross-Attention (DECA) module for cross-modal feature weighting and a Dual-Encoder Feature Aggregation (DEPA) module for hierarchical fusion-where the RGB branch captures texture semantics while the depth branch extracts geometric priors. To comprehensively validate the effectiveness and generalization capability of the proposed framework, we designed a multi-stage evaluation strategy leveraging complementary benchmark datasets. On the M FD dataset, the model was evaluated under both RGB-depth and RGB-infrared configurations to verify core fusion performance and extensibility to diverse modalities. Additionally, the VOC2007 dataset was augmented with pseudo-depth maps generated by Depth Anything, assessing adaptability under monocular input constraints. Experimental results demonstrate that our method achieves mAP50 scores of 82.59% on VOC2007 and 81.14% on M3FD in RGB-infrared mode, outperforming the baseline YOLOv11 by 5.06% and 9.15%, respectively. Notably, in the RGB-depth configuration on M FD, the model attains a mAP50 of 77.37% with precision of 88.91%, highlighting its robustness in geometric-aware detection tasks. Ablation studies confirm the critical roles of the Dynamic Branch Enhancement (DBE) module in adaptive feature calibration and the Dual-Encoder Attention (DEA) mechanism in multi-scale fusion, significantly enhancing detection stability under challenging conditions. With only 2.47M parameters, the framework provides an efficient and scalable solution for high-precision spatial perception in autonomous driving and robotics applications.