Search Results (104)

The number of kernels per ear is a key yield parameter that reflects the effects of breeding and agronomic management practices on crop productivity. However, conventional manual counting is labor-intensive, time-consuming, and prone to human error. This study evaluated the performance of six YOLO models, trained from scratch and fine-tuned, alongside a Faster R-CNN model, for automated kernel detection and counting from manually harvested field corn ear images. Model performance was assessed for predicting the yield and harvest index (HI) of field corn under varying nitrogen and irrigation rates. Results show that models trained with fine-tuning consistently outperform those trained from scratch in both accuracy and computational speed. Among all tested YOLO models, YOLOv11x achieved the highest performance, with a precision of 0.978, a recall of 0.968, a latency of 4.8 ms, and a prediction coefficient of determination (R²_pred) of 0.858 for the test set and 0.890 for cross-year datasets. The YOLOv8x model ranked second, whereas YOLOv10x was the worst-performing model. Compared to YOLO, Faster R-CNN performed poorly. Yield and HI predictions using YOLOv11x achieved R² values of 0.881 and 0.758, respectively, and captured treatment effects. Overall, the findings demonstrate that YOLO-based architecture is highly effective for detecting kernels and predicting yield in precision agriculture applications. Full article

Background: Real-time and accurate analysis of breast ultrasounds is crucial for diagnosis but remains challenging due to issues like low image contrast and operator dependency. This study aims to address these challenges by developing an integrated framework for real-time lesion detection and tracking. Methods: The proposed system combines Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing, a transfer learning-enhanced YOLOv11 model following a continual learning paradigm for cross-center generalization in for lesion detection, and a novel Detection-Based Tracking (DBT) approach that integrates Kernelized Correlation Filters (KCF) with periodic detection verification. The framework was evaluated on a dataset comprising 11,383 static images and 40 ultrasound video sequences, with a subset verified through biopsy and the remainder annotated by two radiologists based on radiological reports. Results: The proposed framework demonstrated high performance across all components. The transfer learning strategy (TL12) significantly improved detection outcomes, achieving a mean Average Precision (mAP) of 0.955, a sensitivity of 0.938, and an F1 score of 0.956. The DBT method (KCF + YOLO) achieved high tracking accuracy, with a success rate of 0.984, an Intersection over Union (IoU) of 0.85, and real-time operation at 54 frames per second (FPS) with a latency of 7.74 ms. The use of CLAHE preprocessing was shown to be a critical factor in improving both detection and tracking stability across diverse imaging conditions. Conclusions: This research presents a robust, fully integrated framework that bridges the gap between speed and accuracy in breast ultrasound analysis. The system’s high performance and real-time efficiency underscore its strong potential for clinical adoption to enhance diagnostic workflows, reduce operator variability, and improve breast cancer assessment. Full article

Pig behavior recognition plays a vital role for early disease detection, animal welfare evaluation, and precision agriculture. Current deep learning methods tend to be complex, parameter intensive, or lack generalization in unstructured farming scenarios, hindering their deployment on resource-limited devices. To address this issue, we propose three optimizations based on the lightweight YOLO11n: (1) embed SCSA-CBAM in C3k2 layers to enhance multi-scale feature discrimination; (2) introduce WFU in the neck for dynamic cross-scale feature integration; and (3) replace standard convolutions in the backbone with WTConv to reduce the computational overhead. Initialized with COCO pre-trained weights, the proposed model employs a two-stage transfer learning approach combined with data augmentation. On a self-built six-category pig behavior dataset based on public datasets of 2480 original images (split into training/validation sets at an 8:2 ratio via stratified random sampling), the optimized YOLO11n-SCSA-WFU-WT achieves an mAP@0.5 of 0.974 and mAP@0.5:0.95 of 0.785, with 3.40 M parameters, 7.8 GFLOPs, and 72.28 FPS, while achieving substantial accuracy improvements over the baseline and maintaining lightweight performance over the baseline. Ablation experiments verify the independent contributions of each module, and comparisons with mainstream models demonstrate a more favorable accuracy–efficiency trade-off. The overall results confirm the effectiveness of our method, which facilitates real-time pig behavior detection in future smart livestock management. Full article

Side-scan sonar is essential to underwater target detection, yet its effectiveness is hindered by scarce annotated data and complex acoustic artifacts. This study systematically evaluates four YOLO variants, YOLOv8n, YOLOv10n, YOLOv11n, and the newly released YOLOv13n, on two public side-scan sonar datasets with limited samples and severe class imbalance. We assess detection accuracy, computational efficiency, inference speed, and transfer learning using COCO pre-trained weights, as well as the impact of optimizer choice between SGD and AdamW. The results reveal distinct strengths: YOLOv8n achieves the fastest inference at 60.98 FPS, with a competitive mAP50 of 0.906, ideal for real-time applications. YOLOv11n offers the best accuracy–efficiency balance, attaining the highest recall of 0.859 and mAP50 of 0.917. YOLOv13n demonstrates exceptional precision of 0.993 and high-IoU localization, with an mAP75 of 0.760. Transfer learning consistently boosts performance, with average mAP50:95 gains exceeding 54% on the more challenging dataset, highlighting its critical role in overcoming data scarcity. SGD generally outperforms AdamW, confirming its suitability as the default optimizer. These findings provide practical guidelines: YOLOv8 for real-time needs, YOLOv11 for balanced performance, and YOLOv13 for precision-critical tasks with ample resources. This work also establishes a benchmark for future underwater autonomous system research. Full article

Lightweight deep-learning models, including MobileNet and LDDm-CNN, have demonstrated significant potential for distinguishing drones from other aerial objects, making them well suited for deployment in resource-constrained environments. However, classification-based approaches face inherent limitations for real-time surveillance, as they rely on prior object cropping or manual region-of-interest extraction and lack the capability to localize drones directly within a complex scene. This limitation significantly restricts their applicability and effectiveness in dynamic and safety-critical environments such as airspace monitoring and critical infrastructure protection, where both recognition and spatial localization are crucial. To address this gap, we proposed LDDm-YOLO, which uses the YOLO-v8n as a compact feature extractor and integrates a lightweight, anchor-free detection head with a shallow feature pyramid for multi-scale object localization. We employed knowledge distillation to transfer rich spatial and semantic features from a larger teacher detector (YOLO-V8x), while incorporating Bayesian optimization for hyperparameter tuning. All experiments were conducted on the Google Colab platform with NVIDIA T4 GPU. The proposed LDDm-YOLO achieves competitive mean Average Precision (mAP = 0.96), Precision 0.92, Recall 0.94, and 127.06 FPS, retaining a smaller model size of only 6.25 MB and low computational complexity (8.9 GFLOPs). These results indicate the potential of the proposed model for edge device deployment. Full article

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set ($N=1434$ events), the proposed two-stage cascade achieves precision $0.983$, recall $0.975$, F1 $0.979$, accuracy $0.986$, and balanced accuracy $0.983$, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention. Full article

Malaria remains a major global health burden, requiring rapid and reliable diagnostic tools to complement or replace labor-intensive manual microscopy. Although deep learning methods have demonstrated strong potential for automated malaria diagnosis, many existing approaches depend on computationally expensive transfer learning architectures or exhibit sensitivity to suboptimal hyperparameter configurations. This study proposes a lightweight automated framework for binary classification of malaria cell images using a custom Convolutional Neural Network (CNN) optimized by a novel Adaptive Marine Predators Algorithm (AMPA). The proposed AMPA integrates a state-aware adaptive control factor that dynamically adjusts step size based on population loss, thereby improving search efficiency and reducing susceptibility to local optima. The framework was evaluated on the NIH Malaria Cell Image Dataset containing 27,558 single-cell images. Experimental results show that the AMPA-optimized CNN achieves a testing accuracy of 95.00% and an Area Under the Curve of 0.986. Comparative experiments indicate that the proposed model outperforms several reported lightweight architectures, including MobileNetV2 (92.00%) and YOLO-based detectors (94.07%), while achieving performance comparable to deeper networks such as VGG-16 (94.88%), with substantially lower computational complexity. The model further attains high sensitivity (0.94) and precision (0.96), supporting its suitability as a robust and resource-efficient approach for automated malaria screening research. Full article

Solar panels are an important device converting light energy into electricity not only from the sun but also from artificial light sources such as light emitting diodes (LEDs) or lasers. Recent advances in solar cell technologies enable them to be flexible, allowing them to be attached to things with different sizes and shapes. Therefore, it is challenging for AI-equipped systems to automatically recognize and distinguish flexible solar panels from other surrounding objects in realistic, complicated environments. Traditional recognition methods usually suffer from low recognition accuracy and high computational cost. Hence, this paper proposes a deep learning method for solar panel recognition using a complete work flow that includes data acquisition and dataset construction, YOLOv8-based model training, real-time solar panel recognition, and extended functionality. The proposed method demonstrates the accurate identification of realistic flat and flexible solar panels, including bent and partially shaded panels, with a mean average precision (mAP)@0.5 of 99.4% and an mAP@0.5:0.95 of 90.4%. The Pareto front for the multi-objective loss function minimization problem is also investigated to determine the optimal set of weighting parameters for the loss components. Furthermore, another functionality is added to detect the sizes of different solar panels if multiple ones co-exist. These features provide a promising foundation for further usage of the proposed deep learning approach to recognize flexible solar panels in realistic contexts. Full article

The high-quality droplet formation in continuous inkjet printing (CIJ) is crucial for precise character deposition on product surfaces. This process, where a piezoelectric transducer perturbs a high-speed ink stream to generate micro-droplets, is highly sensitive to parameters like ink pressure and transducer amplitude. Suboptimal conditions lead to satellite droplet formation and charge transfer issues, adversely affecting print quality and necessitating reliable monitoring. Replacing inefficient manual inspection, this study develops MBSim-YOLO, a deep learning-based method for automated droplet detection. The proposed model enhances the YOLOv8 architecture by integrating MobileNetv3 to reduce computational complexity, a Bidirectional Feature Pyramid Network (BiFPN) for effective multi-scale feature fusion, and a Simple Attention Module (SimAM) to enhance feature representation robustness. A dataset was constructed using images captured by a CCD camera during the droplet ejection process. Experimental results demonstrate that MBSim-YOLO reduces the parameter count by 78.81% compared to the original YOLOv8. At an Intersection over Union (IoU) threshold of 0.5, the model achieved a precision of 98.2%, a recall of 99.1%, and a mean average precision (mAP) of 98.9%. These findings confirm that MBSim-YOLO achieves an optimal balance between high detection accuracy and lightweight performance, offering a viable and efficient solution for real-time, automated quality monitoring in industrial continuous inkjet printing applications. Full article

Traditional Sound Event Detection (SED) approaches are based on either specialized models or these models in combination with general audio embedding extractors. In this article, we propose to reframe SED as an object detection task in the time–frequency plane and introduce a direct adaptation of modern YOLO detectors to audio. To our knowledge, this is among the first works to employ YOLOv8 and YOLOv11 not merely as feature extractors but as end-to-end models that localize and classify sound events on mel-spectrograms. Methodologically, our approach (i) generates mel-spectrograms on the fly from raw audio to streamline the pipeline and enable transfer learning from vision models; (ii) applies curriculum learning that exposes the detector to progressively more complex mixtures, improving robustness to overlaps; and (iii) augments training with synthetic audio constructed under DCASE 2023 guidelines to enrich rare classes and challenging scenarios. Comprehensive experiments compare our YOLO-based framework against strong CRNN and Conformer baselines. In our experiments on the DCASE-style setting, the method achieves competitive detection accuracy relative to CRNN and Conformer baselines, with gains in some overlapping/noisy conditions and shortcomings for several short-duration classes. These results suggest that adapting modern object detectors to audio can be effective in this setting, while broader generalization and encoder-augmented comparisons remain open. Full article

Ocean internal waves occur in stably stratified seawater and play a crucial role in energy cascade, material transport, and military activities. However, the complex and irregular spatial patterns of internal waves pose significant challenges for accurate detection in SAR images when using conventional convolutional neural networks, which often lack adaptability to geometric variations. To address this problem, this paper proposes a refined Faster R-CNN detection framework, termed “rFaster R-CNN”, and adopts a transfer learning strategy to enhance model generalization and robustness. In the feature extraction stage, a backbone network called “ResNet50_CDCN” that integrates the CBAM attention mechanism and DCNv2 deformable convolution is constructed to enhance the feature expression ability of key regions in the images. Experimental results show that in the internal wave dataset constructed in this paper, this network improves the detection accuracy by approximately 3% compared to the original ResNet50 network. At the region proposal stage, this paper further adds two small-scale anchors and combines the ROI Align and FPN modules, effectively enhancing the spatial hierarchical information and semantic expression ability of ocean internal waves. compared with classical object detection algorithms such as SSD, YOLO, and RetinaNet, the proposed “rFaster R-CNN” achieves superior detection performance, showing significant improvements in both accuracy and robustness. Full article

Effective monitoring of protected wildlife is crucial for biodiversity conservation. While camera traps provide valuable data for ecological observation, existing deep learning models often suffer from low accuracy in detecting rare species and high computational costs, hindering their deployment on edge devices. To address these challenges, this study proposes YOLO11-APS, an improved lightweight model for protected wildlife detection. It enhances the YOLO11n by integrating the self-Attention and Convolution (ACmix) module, the Partial Convolution (PConv) module, and the SlimNeck paradigm. These improvements strengthen feature extraction under complex conditions while reducing computational costs. Experimental results demonstrate that YOLO11-APS achieves superior detection performance compared to the baseline model, attaining a precision of 92.7%, a recall of 87.0%, an mAP@0.5 of 92.6% and an mAP@0.5:0.95 of 62.2%. In terms of model lightweighting, YOLO11-APS reduces the number of parameters, floating-point operations, and model size by 10.1%, 11.1%, and 9.5%, respectively. YOLO11-APS achieves an optimal balance between accuracy and model complexity, outperforming existing mainstream lightweight detection models. Furthermore, tests on unseen wildlife data confirm its strong transferability and robustness. This work provides an efficient deep learning tool for automated wildlife monitoring in protected areas, facilitating the development of intelligent ecological sensing systems. Full article

Raspberry cultivation represents a high-value global industry; however, concerns regarding its sustainability have been raised due to the high costs and labour shortages associated with manual harvesting. These challenges represent significant motivators for the development of robotic systems. This review article analyses contemporary robotic harvesting technologies, with a particular focus on integrated systems, machine vision and end-effectors. A review of the relevant literature was conducted in order to identify and compare the main development trends represented by academic and commercial prototypes. The analysis demonstrates that deep learning methodologies, most notably YOLO architectures, predominate within the domain of machine vision, thereby ensuring the effective identification and assessment of fruit ripeness. In order to ensure that the handling of the subject is done in a gentle manner, it is recommended that soft robotic end-effectors which are equipped with sensors and which minimise mechanical damage be used. In view of the fact that the number of studies focusing directly on raspberries is limited, the present study also analyses transferable technologies from other types of soft fruit. Consequently, future research should concentrate on integrating machine vision models that have been trained using raspberries and developing advanced soft end-effectors with integrated tactile sensors. Full article

Accurate and reliable detection of rice diseases and pests is crucial for ensuring food security. However, traditional deep learning methods often suffer from high rates of missed and false detections when dealing with complex field environments, especially in the presence of tiny disease spots, due to insufficient feature extraction capabilities. To address this issue, this study proposes a high-precision rice disease detection method based on ensemble learning and conducts experiments on a self-built dataset of 12,572 images containing five types of diseases and one type of pest. The ensemble learning model is optimized and constructed through a phased approach: First, using YOLOv8s as the baseline, transfer learning is performed with the agriculture-related dataset PlantDoc. Subsequently, a P2 small-object detection head, an EMA mechanism, and the Focal Loss function are introduced to build an optimized single model, which achieves an mAP_0.5 of 0.899, an absolute improvement of 5.5% compared to the baseline YOLOv8s. Then, three high-performance YOLO object detection models, including the improved model mentioned above, are selected, and the Weighted Box Fusion technique is used to integrate their prediction results to construct the final Ensemble-WBF model. Finally, the AP_0.5 and AR_0.5:0.95 of the model reach 0.922 and 0.648, respectively, with absolute improvements of 2.2% and 3.2% compared to the improved single model, further reducing the false and missed detection rates. The experimental results show that the ensemble learning method proposed in this study can effectively overcome the interference of complex backgrounds, significantly improve the detection accuracy and robustness for tiny and similar diseases, and reduce the missed detection rate, providing an efficient technical solution for the accurate and automated monitoring of rice diseases in real agricultural scenarios. Full article

This study introduces an autonomous framework for grasping moving objects on a conveyor belt, enabling unsupervised detection, grasping, and categorization. The work focuses on two common object shapes—cylindrical cans and rectangular cartons—transported at a constant speed of 3–7 cm/s on the conveyor, emulating typical scenarios. The proposed framework combines a vision-based neural network for object detection, a target localization algorithm, and a deep reinforcement learning model for robotic control. Specifically, a YOLO-based neural network was employed to detect the 2D position of target objects. These positions are then converted to 3D coordinates, followed by pose estimation and error correction. A Proximal Policy Optimization (PPO) algorithm was then used to provide continuous control decisions for the robotic arm. A tailored reinforcement learning environment was developed using the Gymnasium interface. Training and validation were conducted on a 7-degree-of-freedom (7-DOF) robotic arm model in the PyBullet physics simulation engine. By leveraging transfer learning and curriculum learning strategies, the robotic agent effectively learned to grasp multiple categories of moving objects. Simulation experiments and randomized trials show that the proposed method enables the 7-DOF robotic arm to consistently grasp conveyor belt objects, achieving an approximately 80% success rate at conveyor speeds of 0.03–0.07 m/s. These results demonstrate the potential of the framework for deployment in automated handling applications. Full article