Search Results (20)

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

Real-time perception of coal–biomass blending during conveyor-belt transport remains challenging because of local aggregation, particle overlap, and illumination variation. In this study, a laboratory-scale conveyor-belt image dataset covering different coal mass fractions, illumination conditions, and particle sizes was constructed. Whole-image classification, cropped-ROI classification, direct regression, horizontal bounding box (HBB)-based detection, oriented bounding box (OBB)-based detection, and RT-DETR-L detection baselines were compared using YOLO-series and auxiliary models. Coal mass fraction was estimated using a frequency-weighted statistical strategy that converts frame-level predictions into continuous estimates. YOLOv8-cls achieved an average RMSE of 13.98 percentage points (pp), indicating the influence of background interference in whole-image classification. Among HBB models, YOLOv8m achieved the lowest mean RMSE of 6.10 pp but required higher computational cost. Compared with YOLOv8n, YOLOv8n-OBB reduced the average RMSE from 9.02 to 6.90 pp by providing a more compact material-region representation and reducing background redundancy. These results show that OBB representation improves the stability of lightweight models. The proposed method provides a feasible vision-based soft-sensing approach for online trend monitoring of coal–biomass blending under lightweight deployment. Full article

Defects in photovoltaic (PV) modules, including hotspots, shading, and diode failures, significantly reduce power-generation efficiency and pose safety risks. This study proposes a real-time detection and localisation framework for PV defects based on infrared images acquired by unmanned aerial vehicles (UAVs). A dedicated dataset of 5583 infrared/visible images was constructed under standardised acquisition conditions. An improved rotating-bounding-box detector, termed YOLO-CLO, was developed upon YOLOv8-OBB by introducing a lightweight C3m module and a shared-convolution LSCD-OBB detection head. The proposed detector attains 99.1% mAP@0.5, 96.7% mAP@0.5:0.95, and 59.88 FPS with only 8.52 M parameters and 23.6 GFLOPs, outperforming the baseline in both accuracy and efficiency. A multi-feature image-processing pipeline combining gradient, grayscale, temperature, and morphological cues identifies hotspots, diode failures, and obstructions with detection accuracies of 96.97%, 100%, and 88.89%, respectively. A component-level localisation strategy integrating GNSS metadata, the Hough transform, and an improved K-means clustering algorithm accurately recovers the row–column index of each defective module within an array. Comparative experiments against YOLOv5 and Faster R-CNN confirm the superiority of the proposed framework. The method offers low hardware dependency and is suitable for engineering deployment in large-scale PV power stations. Full article

Intelligent perception of watermelon targets in complex field environments is a key prerequisite for automated harvesting and future collaborative decision-making with agricultural large models. To address severe leaf occlusion, large pose variation, dense adhesion among adjacent fruits, and the inability of conventional horizontal bounding boxes to accurately represent target orientation under natural cultivation conditions, this paper proposes an improved YOLO11n-OBB-based method for rotated watermelon detection. During data preparation, a semi-automatic annotation strategy combining segmentation-mask assistance with circumscribed rectangle fitting was adopted to efficiently construct a watermelon OBB dataset that closely matches the true physical boundaries of the fruits. On this basis, three structural improvements were introduced to the YOLO11n-OBB baseline: an LSK module was selectively embedded into the middle and later stages of the backbone to enhance adaptive receptive-field modeling and occlusion reasoning in complex bac kgrounds; the original neck structure was replaced with a lightweight BiFPN to strengthen bidirectional feature fusion for targets with large-scale variation in field scenes; and KFIoU Loss was incorporated into the rotated box regression branch to alleviate angle sensitivity and boundary discontinuity, thereby improving the convergence stability of orientation parameter learning. On the constructed watermelon OBB test set, the improved model raised mAP@0.5 (OBB) from 0.871 to 0.931, mAP@0.5:0.95 (OBB) from 0.670 to 0.736, Precision from 0.885 to 0.931, and Recall from 0.849 to 0.908 relative to the YOLO11n-OBB baseline (relative gains of 6.89%, 9.85%, 5.20%, and 6.95%, respectively), while keeping the inference speed at 100 FPS and the parameter count at only 2.71 M. While maintaining a compact model size and high real-time performance, the proposed method significantly improved rotated detection accuracy in crowded and overlapping scenes. In addition, the detection results were encapsulated into a structured JSON perception interface, preliminarily demonstrating the integration pathway of this lightweight front-end for task planning and human–machine collaborative operations with agricultural large models, and indicating its potential for future intelligent agricultural decision-making. Full article

Precise detection of maize root–stem junction is crucial for hole fertilization in maize cultivation. However, maize root–stem junction detection under field conditions is severely affected by soil clods, crop residues, and weeds, and is further complicated by variations in plant morphology, the small scale of targets, and their sparse spatial distribution. To address these issues, an improved model named PGi-YOLO is proposed in this study, based on YOLOv11n-OBB. A P2 high-resolution detection layer is introduced to improve multi-scale feature representation and enhance small-target localization. The C2PSA-iRMB module replaces the original attention module by integrating an inverted residual mobile block (iRMB) mechanism, thereby strengthening global contextual information fusion while preserving its lightweight design. In addition, the Group Shuffle Convolution (GSConv) module is adopted to replace part of the standard convolution operations, reducing computational redundancy and improving inference efficiency. Experimental results show that PGi-YOLO achieves a precision of 92.0%, a recall of 93.4%, and an mAP@0.5 of 96.9%, with parameters of 2.61 M, a model size of 6.0 MB and an inference time of 5.1 ms. Overall, PGi-YOLO achieves a favorable balance between accuracy and efficiency, demonstrating strong robustness for maize root–stem junction detection in complex field environments and providing reliable support for precision agriculture applications. Full article

Rotated object detection is widely adopted in remote sensing to handle arbitrary object orientations and improve localization accuracy. However, existing methods still suffer from limited global context modeling, degraded feature representation under complex backgrounds, and suboptimal optimization caused by task coupling, which jointly restrict detection performance in challenging scenarios. To address these issues, this paper proposes a novel rotated object detection framework, termed LDA-YOLO, which systematically enhances feature modeling and prediction quality. Specifically, a Large Separable Kernel Attention (LSKA) module is introduced to approximate global spatial interactions through a low-rank separable formulation, enabling effective long-range dependency modeling with linear computational complexity. A Dual-Path Feature Refinement (DPFR) module is designed to improve feature representation by decomposing features into complementary subspaces and performing adaptive fusion to suppress redundancy and noise. In addition, an Angle-Aware Decoupled Head (AADH) is developed to explicitly separate classification, localization, and orientation estimation, thereby reducing inter-task interference and improving optimization stability. The proposed method achieves superior performance compared to existing approaches. Specifically, it improves mAP50 by 1.6% over the baseline YOLOv8n-OBB, while maintaining a lightweight design with significantly reduced computational cost. These results indicate that the proposed framework provides an effective solution for rotated object detection in complex remote sensing scenarios. Full article

With the rapid evolution of remote sensing earth observation technology, high-resolution object detection is crucial in military and civilian domains but faces challenges from expansive views and complex backgrounds. Small objects are particularly challenging due to their low pixel coverage, poor textures, and susceptibility to drastic illumination changes and background clutter. To address these problems, this paper proposes MDCA-YOLO for oriented object detection. A Dual-Branch Perception Module (DBPM) is designed utilizing a synergistic mechanism of large-kernel and strip convolutions to establish long-range dependencies, accurately capturing geometric features of tiny objects even in the absence of local details; Multi-Adaptive Selection Fusion (MASF) is proposed to address cross-scale feature loss by adaptively enhancing feature response while suppressing background noise; furthermore, a reconstructed decoupled detection head, CoordAttOBB, significantly improves angle regression accuracy while reducing complexity. Experimental results on the DIOR-R dataset show MDCA-YOLO surpasses YOLO11s, improving mAP50 and mAP50:95 by 2.5% and 2.7%, respectively, effectively proving the algorithm’s superiority in remote sensing tasks. Full article

Accurate and efficient ship detection in remote sensing imagery is a key enabler of intelligent maritime surveillance operations, supporting real-time decision-making in search and rescue, traffic management, and maritime law enforcement. However, remote ship images pose unique challenges for detection. These include densely distributed targets, complex sea-land backgrounds, large aspect ratios, diverse ship geometries, and high color similarity between ships and their surroundings. To address these issues under the computational constraints of unmanned aerial platforms, we propose GTO-YOLO11n, an enhanced YOLOv11n-based detection model tailored for efficient maritime ship sensing. First, we introduce the GatedFDConvBlock, which employs gated convolutional filtering to strengthen feature extraction for small and elongated ships while suppressing background clutter, thereby reducing missed and false detections in dense scenes. Second, we improve the C2PSA module with a dynamic multi-scale attention design, TSSABlock_DMS, to adaptively model cross-scale feature interactions and enhance robustness to complex maritime environments. Third, we replace the original detection head with OBB_ED, a parameter-sharing head that incorporates depthwise separable convolution (DSConv) and an angle prediction branch to lower model complexity while preserving high-quality localization and classification. To verify the performance of the algorithm, we were conducted on the public datasets HRSC2016, HRSC2016-MS, and ShipRSImageNet. The mAP@50 results were 95.2%, 88.3%, and 76.7%, showing improvements of 3.2%, 2.2%, and 2.6% compared to the original YOLOv11n. Full article

Precise localization of the fruit stem picking point is crucial for robots to achieve efficient harvesting operations. However, in unstructured orchard environments, citrus fruit stems are easily obscured by branches and leaves and affected by factors such as overlapping fruits. This leads to poor picking localization accuracy for robots, impacting their autonomous picking efficiency. Therefore, this paper proposes a method for estimating the posture of citrus fruit stems and performing picking operations under environmental occlusion, based on the YOLO-OBB algorithm. First, the YOLOv5s algorithm detects the ROI of citrus, combined with depth information to obtain their 3D point clouds. Second, the OBB algorithm constructs oriented point cloud bounding boxes to determine stem orientation and picking point locations. Finally, through hand–eye pose transformation of the robotic arm, the end-effector is controlled to achieve precise picking operations. Experimental results indicate that the average picking success rate of the YOLO-OBB algorithm reaches 82%, representing a 50% improvement over approaches without fruit stem estimation. This conclusively shows that the proposed algorithm provides precise fruit stem pose estimation, effectively enhancing robotic picking success rates under constrained fruit stem detection conditions. It offers crucial technical support for autonomous robotic harvesting operations. Full article

Background/Objective: Dental implant therapy requires clinicians to identify edentulous regions and adjacent teeth accurately to ensure precise and efficient implant placement. However, this process is time-consuming and subject to operator bias. To address this challenge, this study proposes an AI-assisted detection framework that integrates deep learning and image processing techniques to predict implant placement pathways on dental panoramic radiographs, supporting clinical decision-making. Methods: The proposed framework is first applied to YOLO models to detect edentulous regions and employs image enhancement techniques to improve image quality. Subsequently, YOLO-OBB is utilized to extract pixel-level positional information about neighboring healthy teeth. An implant pathway orientation visualization algorithm is applied to derive clinically relevant implant placement recommendations. Results: Experimental evaluation using YOLOv9m and YOLOv8n-OBB demonstrated stable performance in both recognition and accuracy. The models achieved Precision values of 88.86% and 89.82%, respectively, with an average angular error of only 1.537° compared to clinical implant pathways annotated by dentists. Conclusions: This study presents the first AI-assisted diagnostic framework for DPR-based implant pathway prediction. The results indicate strong consistency with clinical planning, confirming its potential to enhance diagnostic accuracy and provide reliable decision support in implant dentistry. Full article

Maritime unmanned aerial vehicle (UAV) ship detection faces challenges including variations in ship pose and appearance under multiple viewpoints, occlusion and confusion in dense scenes, complex backgrounds, and the scarcity of ship datasets from UAV tilted perspectives. To overcome these obstacles, this study introduces a high-quality dataset named Marship-OBB9, comprising 11,268 drone-captured images and 18,632 instances spanning nine typical ship categories. The dataset systematically reflects the characteristics of maritime scenes under diverse scales, viewpoints, and environmental conditions. Based upon this dataset, we propose a novel detection network named YOLO11-UAVShip. First, an oriented bounding box detection mechanism is incorporated to precisely fit ship contours and reduce background interference. Second, a newly designed CK_DCNv4 module, integrating deformable convolution v4 (DCNv4) and a C3k2 backbone structure, is developed to enhance geometric feature extraction under aerial oblique view. Additionally, for ships with large aspect ratios, SGKLD effectively addresses the localization challenges in dense environments, achieving robust position regression. Comprehensive experimental evaluation demonstrates that the proposed method yields a 2.1% improvement in mAP@0.5 and a 2.3% increase in recall relative to baseline models on the Marship-OBB9 dataset. While maintaining real-time inference speed, our approach greatly enhances detection accuracy and robustness. This work provides a practical and deployable solution for intelligent ship detection in UAV imagery. Full article

Watermelon is a popular fruit, predominantly cultivated in Asian countries. However, the production and harvesting processes present several challenges. Due to its size and weight, manually harvesting watermelons is labor-intensive and costly. In the future, technology is expected to enable robots to harvest watermelons. Therefore, it becomes essential to introduce intelligent systems to effectively identify and locate watermelons in harvesting. This research aims to develop an advanced methodology for watermelon identification and location using You Look Only Once (YOLO)v8 and YOLOv8-oriented bounding box (OBB) algorithms. Furthermore, the simple online and real-time tracking (SORT) algorithm was employed to track and count watermelons and estimate yield. The performance of YOLOv8-OBB was better than that of YOLOv8 and the highest precision (0.938) was achieved by YOLOv8s-OBB. Additionally, the size of each watermelon was measured with both models. The models help farmers find the optimal watermelons for harvest. Full article

Accurate detection of tomato fruits is a critical component in vision-guided robotic harvesting systems, which play an increasingly important role in automated agriculture. However, this task is challenged by variable lighting conditions and background clutter in natural environments. In addition, the arbitrary orientations of fruits reduce the effectiveness of traditional horizontal bounding boxes. To address these challenges, we propose a novel object detection framework named SN-YOLO. First, we introduce the StarNet’ backbone to enhance the extraction of fine-grained features, thereby improving the detection performance in cluttered backgrounds. Second, we design a Color-Prior Spatial-Channel Attention (CPSCA) module that incorporates red-channel priors to strengthen the model’s focus on salient fruit regions. Third, we implement a multi-level attention fusion strategy to promote effective feature integration across different layers, enhancing background suppression and object discrimination. Furthermore, oriented bounding boxes improve localization precision by better aligning with the actual fruit shapes and poses. Experiments conducted on a custom tomato dataset demonstrate that SN-YOLO outperforms the baseline YOLOv8 OBB, achieving a 1.0% improvement in precision and a 0.8% increase in mAP@0.5. These results confirm the robustness and accuracy of the proposed method under complex field conditions. Overall, SN-YOLO provides a practical and efficient solution for fruit detection in automated harvesting systems, contributing to the deployment of computer vision techniques in smart agriculture. Full article

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults. Full article

Monitoring respiratory parameters is essential in pediatric intensive care units (PICUs), yet bedside tidal volume (Vt) measurement is rarely performed due to the need for invasive airflow sensors. We present a real-time, non-contact respiratory monitoring system using the Azure Kinect DK (Microsoft, Redmond, WA, USA) depth camera, specifically designed for use in the PICU. The system automatically tracks thoracic volume variations to derive a comprehensive set of ventilator equivalent parameters: tidal volume, respiratory rate, minute ventilation, inspiratory/expiratory times, I:E ratio, and peak flows. Results are displayed via an ergonomic web interface for clinical use. This system introduces several innovations: real-time estimation of a complete set of respiratory parameters, a novel infrared-based region-of-interest detection method using YOLO-OBBs, enabling robust operation regardless of lighting conditions, even in total darkness, making it ideal for continuous monitoring of sleeping patients, and a pixel-wise 3D volume computation method that achieves a mean absolute error under 5% on tidal volume. The system was evaluated on both a healthy adult (compared to spirometry) and a critically ill child (compared to ventilator data). To our knowledge, this is the first study to validate such a contactless respiratory monitoring system on a non-intubated child in the PICU. Further clinical validation is ongoing. Full article