Search Results (35)

The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems. Full article

This study proposes an integrated computer vision system for automated petrological analysis of tight sandstone micro-structures. The system combines Zero-Shot Segmentation SAM (Segment Anything Model), Mask R-CNN (Region-Based Convolutional Neural Networks) instance segmentation, and an improved MetaFormer architecture with Cascaded Group Attention (CGA) attention mechanism, together with a parameter analysis module to form a hybrid deep learning system. This enables end-to-end mineral identification and multi-scale structural quantification of granulometric properties, grain contact relationships, and pore networks. The system is validated on proprietary tight sandstone datasets, SMISD (Sandstone Microscopic Image Segmentation Dataset)/SMIRD (Sandstone Microscopic Image Recognition Dataset). It achieves 92.1% mIoU segmentation accuracy and 90.7% mineral recognition accuracy while reducing processing time from more than 30 min to less than 2 min per sample. The system provides standardized reservoir characterization through automated generation of quantitative reports (Excel), analytical images (JPG), and structured data (JSON), demonstrating production-ready efficiency for tight sandstone evaluation. Full article

Soldering of printed circuit board (PCB)-based surface-mounted assemblies is a critical process, and to enhance the accuracy of detecting their multi-targeted soldering defects, we propose an automated sample generation method that combines ControlNet and a Stable Diffusion Model. This method can expand the dataset by quickly obtaining sample images with high quality containing both defects and normal detection targets. Meanwhile, we propose the Cascade Mask R-CNN model with ConvNeXt as the backbone, which performs well in dealing with multi-target defect detection tasks. Unlike previous detection methods that can only detect a single component, it can detect all components in the region. The results of the experiment demonstrate that the detection accuracy of our proposed approach is significantly enhanced over the previous convolutional neural network model, with an increase of more than 10.5% in the mean accuracy precision (mAP) and 9.5% in the average recall (AR). Full article

The rapid expansion of the electric vehicle (EV) market has raised significant safety concerns, particularly regarding fires caused by the thermal runaway of lithium-ion batteries. To address this issue, this study investigates the real-time fire detection performance of segmentation-based object detection models for EVs. The evaluated models include YOLOv5-Seg, YOLOv8-Seg, YOLOv11-Seg, Mask R-CNN, and Cascade Mask R-CNN. Performance is analyzed using metrics such as precision, recall, F1-score, mAP50, and FPS. The experimental results reveal that the YOLO-based models outperform Mask R-CNN and Cascade Mask R-CNN across all evaluation metrics. In particular, YOLOv11-Seg demonstrates superior accuracy in delineating fire and smoke boundaries, achieving minimal false positives and high reliability under diverse fire scenarios. Additionally, its real-time processing speed of 136.99 FPS validates its capability for rapid detection and response, even in complex fire environments. Conversely, Mask R-CNN and Cascade Mask R-CNN exhibit suboptimal performance in terms of precision, recall, and FPS, limiting their applicability to real-time fire detection systems. This study establishes YOLO-based segmentation models, particularly the advanced YOLOv11-Seg, as highly effective EV fire detection and response systems. Full article

In the maritime environment, the instance segmentation of small ships is crucial. Small ships are characterized by their limited appearance, smaller size, and ships in distant locations in marine scenes. However, existing instance segmentation algorithms do not detect and segment them, resulting in inaccurate ship segmentation. To address this, we propose a novel solution called enhanced Atrous Spatial Pyramid Pooling (ASPP) feature fusion for small ship instance segmentation. The enhanced ASPP feature fusion module focuses on small objects by refining them and fusing important features. The framework consistently outperforms state-of-the-art models, including Mask R-CNN, Cascade Mask R-CNN, YOLACT, SOLO, and SOLOv2, in three diverse datasets, achieving an average precision (mask AP) score of 75.8% for ShipSG, 69.5% for ShipInsSeg, and 54.5% for the MariBoats datasets. Full article

Logs require multiple verifications to ensure accurate volume and quantity measurements. Log end detection is a crucial step in measuring log volume and counting logs. Currently, this task primarily relies on the Mask R-CNN instance segmentation model. However, the Feature Pyramid Network (FPN) in Mask R-CNN may compromise accuracy due to feature redundancy during multi-scale fusion, particularly with small objects. Moreover, counting logs in a single image is challenging due to their large size and stacking. To address the above issues, we propose an improved log segmentation model based on Cascade Mask R-CNN. This method uses ResNet for multi-scale feature extraction and integrates a hierarchical Convolutional Block Attention Module (CBAM) to refine feature weights and enhance object emphasis. Then, a Region Proposal Network (RPN) is employed to generate log segmentation proposals. Finally, combined with Deep SORT, the model tracks log ends in video streams and counts the number of logs in the stack. Experiments demonstrate the effectiveness of our method, achieving an average precision ($AP$) of 82.3, $A{P}_s$ of 75.3 for small, $A{P}_m$ of 70.9 for medium, and $A{P}_l$ of 86.2 for large objects. These results represent improvements of 1.8%, 3.7%, 2.6%, and 1.4% over Mask R-CNN, respectively. The detection rate reached 98.6%, with a counting accuracy of 95%. Compared to manually measured volumes, our method shows a low error rate of 4.07%. Full article

Farm aerial survey layers can assist in unmanned farm operations, such as planning paths and early warnings. To address the inefficiencies and high costs associated with traditional layer construction, this study proposes a high-precision instance segmentation algorithm based on SparseInst. Considering the structural characteristics of farm elements, this study introduces a multi-scale attention module (MSA) that leverages the properties of atrous convolution to expand the sensory field. It enhances spatial and channel feature weights, effectively improving segmentation accuracy for large-scale and complex targets in the farm through three parallel dense connections. A bottom-up aggregation path is added to the feature pyramid fusion network, enhancing the model’s ability to perceive complex targets such as mechanized trails in farms. Coordinate attention blocks (CAs) are incorporated into the neck to capture richer contextual semantic information, enhancing farm aerial imagery scene recognition accuracy. To assess the proposed method, we compare it against existing mainstream object segmentation models, including the Mask R-CNN, Cascade–Mask, SOLOv2, and Condinst algorithms. The experimental results show that the improved model proposed in this study can be adapted to segment various complex targets in farms. The accuracy of the improved SparseInst model greatly exceeds that of Mask R-CNN and Cascade–Mask and is 10.8 and 12.8 percentage points better than the average accuracy of SOLOv2 and Condinst, respectively, with the smallest number of model parameters. The results show that the model can be used for real-time segmentation of targets under complex farm conditions. Full article

Applying deep learning algorithms in the construction industry holds tremendous potential for enhancing site management, safety, and efficiency. The development of such algorithms necessitates a comprehensive and diverse image dataset. This study introduces the Aerial Image Dataset for Construction (AIDCON), a novel aerial image collection containing 9563 construction machines across nine categories annotated at the pixel level, carrying critical value for researchers and professionals seeking to develop and refine object detection and segmentation algorithms across various construction projects. The study highlights the benefits of utilizing UAV-captured images by evaluating the performance of five cutting-edge deep learning algorithms—Mask R-CNN, Cascade Mask R-CNN, Mask Scoring R-CNN, Hybrid Task Cascade, and Pointrend—on the AIDCON dataset. It underscores the significance of clustering strategies for generating reliable and robust outcomes. The AIDCON dataset’s unique aerial perspective aids in reducing occlusions and provides comprehensive site overviews, facilitating better object positioning and segmentation. The findings presented in this paper have far-reaching implications for the construction industry, as they enhance construction site efficiency while setting the stage for future advancements in construction site monitoring and management utilizing remote sensing technologies. Full article

Objects in cluttered environments may have similar sizes and shapes, which remains a huge challenge for robot grasping manipulation. The existing segmentation methods, such as Mask R-CNN and Yolo-v8, tend to lose the shape details of objects when dealing with messy scenes, and this loss of detail limits the grasp performance of robots in complex environments. This paper proposes a high-performance grasp detection algorithm with a multi-target semantic segmentation model, which can effectively improve a robot’s grasp success rate in cluttered environments. The algorithm consists of two cascades: Semantic Segmentation and Grasp Detection modules (SS-GD), in which the backbone network of the semantic segmentation module is developed by using the state-of-the-art Swin Transformer structure. It can extract the detailed features of objects in cluttered environments and enable a robot to understand the position and shape of the candidate object. To construct the grasp schema SS-GD focused on important vision features, a grasp detection module is designed based on the Squeeze-and-Excitation (SE) attention mechanism, to predict the corresponding grasp configuration accurately. The grasp detection experiments were conducted on an actual UR5 robot platform to verify the robustness and generalization of the proposed SS-GD method in cluttered environments. A best grasp success rate of 91.7% was achieved for cluttered multi-target workspaces. Full article

The accurate instance segmentation of individual crop plants is crucial for achieving a high-throughput phenotypic analysis of seedlings and smart field management in agriculture. Current crop monitoring techniques employing remote sensing predominantly focus on population analysis, thereby lacking precise estimations for individual plants. This study concentrates on maize, a critical staple crop, and leverages multispectral remote sensing data sourced from unmanned aerial vehicles (UAVs). A large-scale SAM image segmentation model is employed to efficiently annotate maize plant instances, thereby constructing a dataset for maize seedling instance segmentation. The study evaluates the experimental accuracy of six instance segmentation algorithms: Mask R-CNN, Cascade Mask R-CNN, PointRend, YOLOv5, Mask Scoring R-CNN, and YOLOv8, employing various combinations of multispectral bands for a comparative analysis. The experimental findings indicate that the YOLOv8 model exhibits exceptional segmentation accuracy, notably in the NRG band, with bbox_mAP50 and segm_mAP50 accuracies reaching 95.2% and 94%, respectively, surpassing other models. Furthermore, YOLOv8 demonstrates robust performance in generalization experiments, indicating its adaptability across diverse environments and conditions. Additionally, this study simulates and analyzes the impact of different resolutions on the model’s segmentation accuracy. The findings reveal that the YOLOv8 model sustains high segmentation accuracy even at reduced resolutions (1.333 cm/px), meeting the phenotypic analysis and field management criteria. Full article

The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model’s receptive field, and MDConv is introduced to enhance the model’s feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP_0.5, and AP_0.75 compared to the existing Cascade Mask R-CNN model. Full article

Blueberry fruit phenotypes are crucial agronomic trait indicators in blueberry breeding, and the number of fruits within the cluster, maturity, and compactness are important for evaluating blueberry harvesting methods and yield. However, the existing instance segmentation model cannot extract all these features. And due to the complex field environment and aggregated growth of blueberry fruits, the model is difficult to meet the demand for accurate segmentation and automatic phenotype extraction in the field environment. To solve the above problems, a high-precision phenotype extraction model based on hybrid task cascade (HTC) is proposed in this paper. ConvNeXt is used as the backbone network, and three Mask RCNN networks are cascaded to construct the model, rich feature learning through multi-scale training, and customized algorithms for phenotype extraction combined with contour detection techniques. Accurate segmentation of blueberry fruits and automatic extraction of fruit number, ripeness, and compactness under severe occlusion were successfully realized. Following experimental validation, the average precision for both bounding boxes (bbox) and masks stood at 0.974 and 0.975, respectively, with an intersection over union (IOU) threshold of 0.5. The linear regression of the extracted value of the fruit number against the true value showed that the coefficient of determination (R²) was 0.902, and the root mean squared error (RMSE) was 1.556. This confirms the effectiveness of the proposed model. It provides a new option for more efficient and accurate phenotypic extraction of blueberry clusters. Full article

In order to improve the efficiency and accuracy of multitarget detection of soldering defects on surface-mounted components in Printed Circuit Board (PCB) fabrication, we propose a sample generation method using Stable Diffusion Model and ControlNet, as well as a defect detection method based on the Swin Transformer. The method consists of two stages: First, high-definition original images collected in industrial production and the corresponding prompts are input to Stable Diffusion Model and ControlNet for automatic generation of nonindependent samples. Subsequently, we integrate Swin Transformer as the backbone into the Cascade Mask R-CNN to improve the quality of defect features extracted from the samples for accurate detection box localization and segmentation. Instead of segmenting individual components on the PCB, the method inspects all components in the field of view simultaneously over a larger area. The experimental results demonstrate the effectiveness of our method in scaling up nonindependent sample datasets, thereby enabling the generation of high-quality datasets. The method accurately recognizes targets and detects defect types when performing multitarget inspection on printed circuit boards. The analysis against other models shows that our improved defect detection and segmentation method improves the Average Recall (AR) by 2.8% and the mean Average Precision (mAP) by 1.9%. Full article

The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic breeding data. Therefore, the identification of ear tag dropout is crucial for intelligent breeding in pig farms. In the production environment, promptly detecting breeding pigs with missing ear tags is challenging due to clustering overlap, small tag targets, and uneven sample distributions. This study proposes a method for detecting the dropout of breeding pigs’ ear tags in a complex environment by integrating an attention mechanism. Firstly, the approach involves designing a lightweight feature extraction module called IRDSC using depthwise separable convolution and an inverted residual structure; secondly, the SENet channel attention mechanism is integrated for enhancing deep semantic features; and finally, the IRDSC and SENet modules are incorporated into the backbone network of Cascade Mask R-CNN and the loss function is optimized with Focal Loss. The proposed algorithm, Cascade-TagLossDetector, achieves an accuracy of 90.02% in detecting ear tag dropout in breeding pigs, with a detection speed of 25.33 frames per second (fps), representing a 2.95% improvement in accuracy, and a 3.69 fps increase in speed compared to the previous method. The model size is reduced to 443.03 MB, a decrease of 72.90 MB, which enables real-time and accurate dropout detection while minimizing the storage requirements and providing technical support for the intelligent breeding of pigs. Full article

Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior–posterior (AP) radiography image. We introduce an encoder–decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks. Full article