Search Results (3,883)

Early plant leaf disease detection and timely control is important for agricultural yield and stability. Yet, it is difficult for manual labor to monitor the health of the plant leaf 24 h a day. Existing detection approach cannot meet the demands of texture enhancement features. Therefore, this paper proposes a new detection approach which undergoes three-layer transformations: convolutional layer, attention mechanism layer and loss function layer. Firstly, ADown is used to extract fine-grained texture features from suspected leaves to reduce computational load. Secondly, Gabor texture enhancement is proposed to extract and enhance the contour and the directional texture of suspected areas using multi-directional filtering, followed by a combination Transformer to enhance the global context modeling capability. Thirdly, a dynamic boundary loss function (DBL) is employed to dynamically adjust the probability distribution of bounding box regression through adaptive temperature coefficient and information entropy, thereby improving the positioning accuracy of the detection box. The experiments show that ATD-Net achieved an average accuracy of 87.42% (mAP50) and an accuracy of 85.96%, with a computational complexity of 6.5 GFLOPs. The visualization results and ablation experiments show that the collaborative work of the proposed modules significantly improves the detection robustness in complex backgrounds, early diseases, and small target scenes. Compared to the original model, ATD-Net achieves a performance improvement of 1.1% at mAP50 and a speed increase of 17.7%. The model size remains almost unchanged, at 5.2 MB. It is an efficient and promising solution for future real-time disease recognition in complex agricultural environments. Full article

Accurate identification of plant numbers is crucial for evaluating the effectiveness of mechanical rice seedling transplanting, which directly affects yield estimation and replanting decisions in precision agriculture. Conventional manual counting methods are time-consuming and labor-intensive, which hinders their application in modern agriculture, where efficiency and precision are paramount. Therefore, this study constructed a dataset based on images collected by consumer-grade Unmanned Aerial Vehicles (UAVs) and proposed an improved lightweight detection model named CLR-YOLO (Complex-scene Lightweight Rice-detection YOLO) based on the YOLOv11n. The model replaces the original C3k2 module with C3k2-PConv to improve computational efficiency while maintaining feature extraction capability. Additionally, it reconstructs the neck network using the Heterogeneous Selective Feature Pyramid Network (HSFPN) to optimize the handling of features from both large and small targets. Finally, the PConvHead detection head is designed to enhance feature utilization efficiency and reduce both false positives and missed detections in dense rice seedling scenarios. Experimental results demonstrated that CLR-YOLO achieved an average precision (AP@0.5) of 93.9%. While maintaining comparable accuracy, the model reduced parameters to 1.4 M, computational cost to 3.7 GFLOPs, and model size to 2.9 MB—reductions of 46.2%, 41.3%, and 44.2%, respectively, compared to the baseline model. This model provides significant support for rice seedling detection and offers valuable insights to assist agricultural producers in making subsequent decisions. Full article

Addressing the challenges of complex background interference, low lighting conditions, small target recognition, and difficulty in maturity grading in the automated detection of Pleurotus ostreatus, this study proposes a lightweight improved scheme based on color feature enhancement. By collecting 4779 images from five developmental stages in three typical planting environments, including greenhouses and mushroom houses, an HSV hue analysis database was established to determine key hue intervals [4°, 38°] or [110°, 155°] for different environments. Secondly, based on the hue interval distribution of Pleu-rotus ostreatus, YOLOv13 was used as the base model, with the addition of an HSV hue mask as the fourth channel to improve the input layer. The custom ColorWeight module was used to enhance color feature expression; the hypergraph computation module was improved to enhance feature correlation; and the neck network incorporated the StockenAttention module to improve the ability to capture maturity features. The accuracy of the improved model was increased to 89.5% in mAP@0.5 (+3.3%), surpassing the mainstream YOLOv8n-12n series. Efficiency optimization achieved real-time detection at 12.58 FPS on the RTX3090Ti platform. In practical applications, the accuracy of maturity recognition was significantly improved, with a 73.6% decrease in the misclassification rate of maturity and a reduction in missed detections, achieving an F1 score of 0.91. In conclusion, through the deep integration of Hue features and deep learning models, while ensuring lightweight deployment (with only a 10.5% increase in parameter count), the accuracy and practicality of Pleurotus ostreatus detection were significantly improved, providing an effective solution for intelligent mushroom house management. Full article

The widespread deployment of unmanned aerial vehicles (UAVs) in civil and commercial airspace has raised significant safety concerns, driving the demand for reliable and real-time Anti-UAV visual detection systems. However, existing deep learning-based detectors face substantial challenges in complex low-altitude environments, including drastic scale variations, severe background clutter, and weak feature representation of small UAV targets. Moreover, handcrafted Transformer-based architectures often lack adaptability across diverse scenarios and struggle to balance detection accuracy with computational efficiency. To address these limitations, this paper proposes AutoUAVFormer, a super-resolution guided neural architecture search framework for Anti-UAV detection. In contrast to conventional manually designed approaches, AutoUAVFormer leverages joint optimization of a Transformer-based detection objective and a super-resolution reconstruction objective to automatically identify a task-specific optimal network architecture for detecting UAV targets. Specifically, a unified search space is formulated by jointly embedding Transformer hyperparameters and Feature Pyramid Network (FPN) structures, facilitating end-to-end co-optimization of multi-scale feature fusion and global context modeling. To efficiently locate architectures that balance accuracy and computational cost, a three-stage pipeline, combining supernetwork training with evolutionary search, is employed. Additionally, we design a super-resolution auxiliary branch that operates only during training to enhance the model’s ability to learn fine-grained textures and sharpen edge representations of small targets, without introducing any inference overhead. Extensive experiments on three challenging Anti-UAV detection benchmarks, namely DetFly, DUT Anti-UAV, and UAV Swarm, confirm the superiority of AutoUAVFormer over current state-of-the-art methods, with mAP@0.5 scores reaching 98.6%, 95.5%, and 89.9% on the respective datasets while sustaining real-time inference speed. These results demonstrate that AutoUAVFormer achieves strong generalization and maintains robust Anti-UAV detection performance under challenging low-altitude conditions. Full article

Sustainable precision agriculture is crucial for optimizing resource utilization, reducing chemical inputs, and ensuring global food security. High-precision automatic recognition and monitoring of key crop organs (e.g., wheat heads and flower clusters) serve as the technological foundation for sustainable agricultural management decisions. However, visual perception in natural field environments is highly susceptible to external conditions. To address the challenges of severe background interference and feature dilution in crop small object detection within complex agricultural scenarios, this paper proposes an enhanced detection network, ACF-YOLO, based on YOLO11. First, an Aggregated Multi-scale Local-Global Attention (AMLGA) module is designed to enhance the feature representation of weak targets by fusing local details with global semantics. Second, a Context-Guided Fusion Module (CGFM) and a Soft-Neighbor Interpolation (SNI) strategy are introduced. Their synergy alleviates feature aliasing effects and ensures the precise alignment of deep semantic information with shallow spatial details. Furthermore, the Inner-MPDIoU loss function is employed to optimize the bounding box regression accuracy for non-rigid targets by incorporating geometric constraints and auxiliary scale factors. To verify the detection capability of the proposed method, we constructed a UAV Wheat Head Dataset (UWHD) and conducted extensive experiments on the UWHD, GWHD2021, and RFRB datasets. The experimental results demonstrate that ACF-YOLO outperforms other comparative methods, confirming its stable detection performance and contributing to the sustainable development of agriculture. Full article

Background: Familial hemiplegic migraine (FHM) is a rare and severe form of migraine disorder featuring aura symptoms that include hemiplegia during attacks. While pathogenic missense variants in CACNA1A, ATP1A2, and SCN1A can cause FHM or its sporadic form, they explain less than 20% of suspected hemiplegic migraine cases, suggesting the involvement of other genes or genetic variations, potentially including copy number variations (CNVs). PPRT2 gene variants including CNVs have also been implicated in hemiplegic migraine. Methods: Multiplex ligation-dependent probe amplification (MLPA) assays were used to investigate the presence of CNVs in the CACNA1A, SCN1A, ATP1A2, and PRRT2 genes in a cohort of 170 unrelated probands suspected to have FHM who had tested negative for pathogenic missense or small indel variants within these genes. Potential CNVs were subsequently confirmed using quantitative PCR. Results: In 15 patients referred for FHM genetic testing, various CNVs in the target genes were detected by MLPA and subsequently validated by quantitative PCR. CACNA1A exon duplications were identified in six patients and deletions found in two. Two patients had ATP1A2 exon deletions, while one had a duplication. For SCN1A, exon deletions were found in three patients and a duplication in one. PRRT2 exon deletions were detected in five patients, with a single nucleotide polymorphism (SNP) array confirming a deletion spanning PRRT2 and neighbouring loci including 26 genes in one of those. Three patients had CNVs in more than one FHM gene. Conclusions: Our study demonstrates the presence of CNVs in FHM genes in a subset of hemiplegic migraine cases (~9%), suggesting a likely role in the disorder and highlighting the need to explore structural variation in addition to the commonly interrogated genetic mutation points. These findings contribute to further understanding of genetic mechanisms that underlie hemiplegic migraine and may inform improved diagnostic and therapeutic strategies. Full article

Precision-targeted pollination places strict demands on flower organ detection in field environments. In field conditions, pistil detection in Siraitia grosvenorii remains difficult because the targets are small, often occluded, and require accurate localization under lightweight model constraints. To address these challenges, we develop an improved YOLOv11n-based method for Siraitia grosvenorii pistil detection in precision pollination tasks. The model incorporates SCConv, CoordAtt, and SimAM to improve feature extraction and foreground discrimination for small pistil targets in complex backgrounds. The main contribution of this work lies in task-oriented module integration and lightweight optimization for tiny pistil detection, rather than in proposing a new generic detection operator. Experiments on the self-built dataset show that the improved model achieves 82.17% mAP@0.5, 40.38% mAP@0.5:0.95, and 86.20% precision, improving upon the YOLOv11n baseline by 2.92, 2.28, and 9.84 percentage points, respectively. Recall decreases from 78.46% to 76.50%, suggesting a precision-oriented trade-off in the current setting. With only 2.89 M parameters and 7.22 GFLOPs, the model maintains a lightweight architecture while achieving improved detection performance for targeted pollination tasks. These findings support the feasibility of the proposed method for Siraitia grosvenorii pistil detection in intelligent pollination applications. Full article

The hERG potassium channel is critical for cardiac ventricular repolarization and a core target in pre-clinical drug safety screening. A robust, stable cell line with uniform, high hERG expression is essential for high-throughput assessments. In this study, we established a functional stable HEK293T cell line with high hERG expression. The hERG gene was subcloned into Lenti-HA-hERG-P2A-EGFP plasmid, in which GFP serves as a selection marker via a P2A self-cleaving peptide. GFP-positive monoclonal cells were isolated by fluorescence-activated cell sorting (FACS). Confocal imaging confirmed that hERG localized predominantly to the cell membrane, consistent with its physiological role. Manual patch-clamp revealed canonical hERG current properties: a small, stable current during depolarization to 20 mV, followed by a large outward tail current upon repolarization to −40 mV-a hallmark of hERG channel gating. Automated patch-clamp (APC)-based current profiling showed 93.5% of stable hERG cells exhibited peak tail currents >50 pA (87% > 100 pA, with 49.5% > 400 pA), whereas 100% of blank HEK293T cells showed peak tail currents < 50 pA. Pharmacological validation with E-4031 demonstrated concentration-dependent inhibition of hERG currents, with an IC₅₀ of 29.8 nM, which is consistent with literature-reported values. The stable hERG-expressing HEK293T cell line developed here exhibits consistent hERG expression, canonical channel function, and physiological sensitivity to hERG blockers. When paired with high-throughput APC systems, this cell model provides a robust, standardized platform for pre-clinical drug-induced hERG inhibition evaluation, aiding early detection of long QT syndrome risks and safer drug development. Full article

Synthetic aperture radar (SAR) ship detection in complex inshore scenarios has long been constrained by the coupled effects of speckle noise and small–scale weak scattering targets. Although feature–level frequency–domain denoising methods partially alleviate noise interference, existing studies predominantly focus on spatial frequency modeling and implicitly assume consistent spectral responses and discriminative contributions across channels. This assumption may lead to over–suppression of weak ship targets under complex backgrounds. To address the incomplete dimensionality of current frequency–domain modeling, this paper proposes FSMD–Net, a joint spatial–channel spectral modeling framework for SAR ship detection. During multi–scale feature fusion, a coordinated modulation mechanism integrating multi–spectral channel attention with spatial frequency–domain denoising is introduced. This design enables channel discriminability and frequency–subspace denoising to act synergistically, enforcing structurally consistent spectral constraints throughout multi–scale feature propagation. Extensive experiments on SARDet–100K, HRSID, and AIR–SARShip–2.0 demonstrate that FSMD–Net achieves consistent performance improvements, particularly in small–target and strong–clutter scenarios, exhibiting enhanced detection accuracy and robustness. Full article

Digital electricity meters display readings via digits, but accurate image-based recognition faces a key challenge: the frequent omission of decimal points creates a critical asymmetry between the visual image and its true semantic meaning. To address this visual-semantic asymmetry, we propose an improved YOLOv10n approach incorporating cascaded Visual-Semantic processing. We introduce a Reparameterized Convolution Single-Shot Aggregation (RCSOSA) module and a SimAM attention mechanism to enhance feature extraction, and employ Normalized Wasserstein Distance (NWD) Loss to boost small-target detection. To rectify the visual-semantic asymmetry, we introduce domain-specific format rules based on power industry standards (taking GB/T 17215-2018 as an example) to provide structural constraints for digit recognition. Experimental results show superior performance with 0.870 precision, 0.932 mAP50, and 116 FPS inference speed, outperforming reference models in both precision and efficiency for real-time meter inspection. Full article

Existing technologies can achieve relative geometric correction and stabilization of geostationary satellite image sequences through fixed land scene matching or homonymous point adjustment. However, these methods heavily rely on fixed land areas, rendering them completely ineffective in vast ocean regions with only ship targets. Additionally, the trajectories of ship targets after processing still exhibit noticeable jitter, hindering motion information analysis. To address these issues, this paper proposes a joint image adjustment and stabilization method based on multi-target trajectories in marine environments: (1) An optimized target detection algorithm based on a multi-scale heterogeneous convolution module is introduced, which extracts background and target features through convolutions of different scales, enabling accurate detection and tracking of weak small targets in the image sequence frame by frame. (2) Curve fitting is performed on the detected positions of the same ship across multiple frames to simulate its motion trajectory under stabilized conditions. Combined with the prior assumption of uniform motion, an equal-division strategy is adopted to determine the corrected positions of the target in the image sequence. (3) The deviation correction values of multiple targets within the same frame are obtained, and based on the principle of intra-frame deviation consistency, precise image stabilization is achieved under multi-target constraints. Experiments based on Gaofen-4 satellite image sequences demonstrate that this method reduces the average position deviation of ship targets in the original images from 8.5 pixels (425 m) to 3.4 pixels (170 m), a decrease of approximately 59.41%, effectively improving the relative geometric accuracy of the image sequence and significantly eliminating target trajectory jitter. Full article

In remote sensing image oriented object detection tasks, weakly supervised learning methods based on horizontal bounding boxes have attracted much attention due to their lower annotation costs compared to fully supervised methods. However, remote sensing images, characterized by complex backgrounds, exhibit a wide range of target scales and diverse geometric characteristics across target categories. Existing methods exhibit inadequate exploitation of background and angular information under weak supervision, resulting in compromised perception of dense and high-aspect-ratio targets. Neglecting the imbalance in angle estimation samples further leads to excessively low detection accuracy for few-shot categories. To address the aforementioned issues, this paper proposes a Geometry-Aware Enhancement Network (WSOOD-GAEN) for weakly supervised oriented object detection tasks. First, in the backbone network stage, a channel-space deformable attention module (DAE-ResNet) was constructed. Through deformable sampling and screening of key regions, feature extraction has both morphological adaptability to complex shapes and semantic discriminability of key features in complex backgrounds. Secondly, in the feature pyramid stage, an Angle-Guided Feature Pyramid Network (AG-FPN) is proposed. This module dynamically applies rotation transformation to the sampling offsets of deformable convolutions, thereby enhancing the feature representation of objects with different orientations and scales. Furthermore, an adaptive geometric perception loss (AGL) was designed. Based on the geometric characteristics of different categories, it automatically learns differentiated rotation and flip consistency weights, thereby improving the prediction accuracy of small sample categories. Experiments on the DOTA-v1.0, HRSC, and RSAR datasets validate our approach. Specifically, under the AP75 evaluation metric, the proposed method outperforms existing weakly supervised methods by 1.51%, 9.86%, and 3.28%, respectively. Full article

For vehicle driving scenarios in complex backgrounds, road sign detection faces challenges such as multi-scale targets, long-distances, and low-resolution. To address these challenges, a detection method based on an improved YOLO11n network is proposed. Firstly, to accommodate the multi-scale characteristics of the targets and improve the network’s ability to detect low-resolution objects and details, a Multi-path Gated Aggregation (MGA) Module is proposed, achieving these objectives via multi-dimensional feature extraction. Secondly, the Neck is improved by designing a network structure that incorporates high-resolution information from the Backbone, thereby enhancing the detection capabilities for small and blurry targets. Finally, an enhanced Spatial Pyramid Pooling—Fast (SPPF) module is proposed, wherein a Group Convolution-Layer Normalization-SiLU structure is integrated across various stages of information passing. By fusing adjacent channel information, it effectively suppresses complex background noise across multiple scales and amplifies road marking features, which consequently boosts the model’s discriminability for distant and obscured targets. Experimental results on a multi-type road sign dataset show that the improved model achieves an mAP@0.5 of 96.96%, which is 1.42% higher than the original model. The mAP@0.5–0.95 and Recall rates are 83.94% and 92.94%, respectively, while the inference speed remains at 134 FPS. Research demonstrates that via targeted modular designs, the proposed approach strikes a superior balance between detection accuracy and real-time efficiency. Consequently, it provides robust technical support for the reliable operation of intelligent vehicle perception systems under complex conditions. Full article

In recent years, deep learning has been applied more and more to medical image analysis. One such application of deep learning is the automated polyp detection in colonoscopy with the target of reducing miss rates. This study presents a comprehensive evaluation of nine state-of-the-art object detection models for colonic polyp detection: YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO12, YOLO26, RT-DETR, YOLO-World, and YOLOE. The models were evaluated on three publicly available datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. All models were trained under standardized conditions using identical hyperparameters and data augmentation strategies to guarantee fair comparison. Performance was evaluated using multiple metrics: mAP@50, mAP@50–95, F1 score, precision, recall, inference time, and computational cost. YOLO11 demonstrated the best overall performance, achieving mAP@50 scores of 0.995, 0.944, and 0.978 on the three datasets respectively, while maintaining the fastest inference time of approximately 150 ms per image and the third-lowest computational cost at 21.3 GFLOPs. Cross-dataset generalization experiments revealed a significant loss of performance, with mAP@50 dropping by 20–40% when models were tested on an unseen dataset, highlighting the challenge of true generalization with limited datasets. Statistical analysis by polyp size showed that while all models achieved F1 scores exceeding 0.95 for large polyps, performance decreased to 0.60–0.85 for small polyps, indicating a limitation in detecting small lesions. The analysis of failure modes showed that missed detections, false positives and boundary errors constitute 60–75% of all failures, suggesting that domain adaptation of object detection models may be required. Full article

Accurate detection of prostate cancer suspicious areas in biparametric MRI (bpMRI) remains challenging because of severe lesion-to-background imbalance, limited lesion contrast, and inter-reader variability in lesion delineation. Unlike prior approaches that collapse inter-reader disagreement into a single consensus label, this study makes three contributions: (1) an adapted nnU-Net framework with prostate-centered preprocessing to reduce voxel-level class imbalance; (2) a class-imbalance-aware composite loss combining Dice, binary cross-entropy, and tailored focal loss to improve sensitivity to small and low-contrast lesions; and (3) a multi-expert learning strategy that preserves reader-specific annotations as separate supervision targets and aggregates predictions at the ensemble level. The method was developed on a single-center dataset of 378 bpMRI studies independently annotated by three board-certified radiologists. Of these, 323 studies were used for model development with patient-level 5-fold cross-validation, and 55 studies were reserved as a fixed independent test set. Compared with our previously published U-Net baseline, the proposed consensus-based nnU-Net improved Average Precision (AP) from 0.69 to 0.75, AUROC from 0.92 to 0.96, and the PI-CAI score from 0.81 to 0.85 on the independent test set. In addition, the multi-expert approach further improved AP to 0.81 versus 0.76 (+6.6%, p < 0.01), AUROC to 0.99 versus 0.95 (+4.2%, p < 0.01), and the PI-CAI score to 0.90 versus 0.86 (+4.7%). These findings demonstrate that explicitly preserving expert disagreement as a training signal, combined with anatomically targeted preprocessing and tailored loss design, substantially improves prostate lesion detection in bpMRI, providing a strong basis for future multi center external validation. Full article