Mean Average Precision Score Research Articles

Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs

Rapid technological advances and growing participation from amateur naturalists have made countless images of insects in their natural habitats available on global web portals. Despite advances in automated species identification, traits like developmental stage or health remain underexplored or manually annotated, with limited focus on automating these features. As a proof-of-concept, we developed a computer vision model utilizing the YOLOv5 algorithm to accurately detect monarch butterfly caterpillars in photographs and classify them into their five developmental stages (instars). The training data were obtained from the iNaturalist portal, and the photographs were first classified and annotated by experts to allow supervised training of models. Our best trained model demonstrates excellent performance on object detection, achieving a mean average precision score of 95% across all five instars. In terms of classification, the YOLOv5l version yielded the best performance, reaching 87% instar classification accuracy for all classes in the test set. Our approach and model show promise in developing detection and classification models for developmental stages for insects, a resource that can be used for large-scale mechanistic studies. These photos hold valuable untapped information, and we’ve released our annotated collection as an open dataset to support replication and expansion of our methods.

Unraveling Indonesian heritage through pattern recognition using YOLOv5

This research focuses on three iconic Indonesian batik patterns-Kawung, Mega Mendung, and Parang-due to their cultural significance and recognition. Kawung symbolizes harmony, Mega Mendung represents power, and Parang signifies protection and spiritual power. Using the YOLOv5 deep learning model, the study aimed to accurately identify these patterns. Results showed mean average precision (mAP) scores of 77% for Kawung, 80% for Parang, and an impressive 99% for Mega Mendung. The highest precision results were 91% for Kawung, 88% for Parang, and 77% for Mega Mendung. These findings highlight the potential of pattern recognition in preserving cultural heritage. Understanding these designs contributes to the appreciation of Indonesia s culture. The research suggests applications in cultural studies, digital archiving, and the textile industry, ensuring the legacy of these patterns endures.

Facial Expression Recognition-You Only Look Once-Neighborhood Coordinate Attention Mamba: Facial Expression Detection and Classification Based on Neighbor and Coordinates Attention Mechanism.

In studying the joint object detection and classification problem for facial expression recognition (FER) deploying the YOLOX framework, we introduce a novel feature extractor, called neighborhood coordinate attention Mamba (NCAMamba) to substitute for the original feature extractor in the Feature Pyramid Network (FPN). NCAMamba combines the background information reduction capabilities of Mamba, the local neighborhood relationship understanding of neighborhood attention, and the directional relationship understanding of coordinate attention. The resulting FER-YOLO-NCAMamba model, when applied to two unaligned FER benchmark datasets, RAF-DB and SFEW, obtains significantly improved mean average precision (mAP) scores when compared with those obtained by other state-of-the-art methods. Moreover, in ablation studies, it is found that the NCA module is relatively more important than the Visual State Space (VSS), a version of using Mamba for image processing, and in visualization studies using the grad-CAM method, it reveals that regions around the nose tip are critical to recognizing the expression; if it is too large, it may lead to erroneous prediction, while a small focused region would lead to correct recognition; this may explain why FER of unaligned faces is such a challenging problem.

Intelligent Vision System with Pruning and Web Interface for Real-Time Defect Detection on African Plum Surfaces

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at a large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG-16, DenseNet-121, MobileNet, and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models, respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10–30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.

YOLOrot2.0: A novel algorithm for high-precision rice seed size measurement with real-time processing

We present YOLOrot2.0, an automated algorithm for measuring the dimensions of rice seeds, which holds significant importance in evaluating grain quality and genetic breeding research. The current methods for rice seed measurement exhibit various limitations, such as the need for seed preprocessing and selection, specialized equipment for measurement, and prolonged measurement time. To address these issues, YOLOrot2.0 introduces the following improvements: Firstly, it utilizes an anchor-free detection algorithm to optimize the detection of rotated targets. Secondly, the utilization of the Kalman Filter IoU loss function, which combines the horizontal bounding box and smooth L1 loss function, accelerates the convergence speed of the network. Moreover, the YOLOv8 architecture was adjusted by modifying certain convolutional layers and the Context to Fusion module to enhance the detection capabilities for smaller targets. Finally, YOLOrot2.0 enables direct processing of the entire image, eliminating the need for image partitioning and resulting in substantial time and processing savings. Experimental results demonstrate that YOLOrot2.0 achieves an mAP (mean average precision) score of 0.95, surpassing other comparative algorithms. Additionally, YOLOrot2.0 provides highly accurate measurements of seed length and width, closely aligned with ground truth values. Notably, YOLOrot2.0 achieves real-time detection by achieving an average detection time of only 11.5 ms for images sized 3000×4000 pixels.

Falling Detection of Toddlers Based on Improved YOLOv8 Models.

If toddlers are not promptly checked and rescued after falling from relatively high locations at homes, they are at risk of severe health complications. We present a toddler target extraction method and real-time falling alarm. The procedure is executed in two stages: In stage I, a GELAN-integrated YOLOv8 model is used to extract the body features. Based on this, a head capture technique is developed to obtain the head features. In stage II, the "safe zone" is calculated through Generalized Hough Transform (GHT). The spatial location is compared to the preceding stage's two centers of mass points, K for the toddler's body and H for the head. Position status detection is performed on the extracted data. We gathered 230 RGB-captured daily videos of toddlers aged 13 to 30 months playing and experiencing upside-down falls. We split 500 video clips (×30 FPS) from 200 videos into 8:2 training and validation sets. A test set of 100 clips (×30 FPS) was cut from another 30 videos. The experimental results suggested that the framework has higher precision and recall in detection, as well as improved mean average precision and F1 scores compared to YOLOv3, v5, v6, and v8. It meets the standard FPS requirement for surveillance cameras and has an accuracy of 96.33 percent.

Improved Road Target Detection Algorithm for YOLOv7-Tiny

In complex road scenes, we propose an enhanced road target detection algorithm for YOLOv7-Tiny to address issues such as large model size, misclassification, and low localization accuracy. Our approach involves several key modifi-cations. Firstly, we replace the LeakyReLU activation function in YOLOv7-Tiny with H-swish. This replacement not only reduces the number of parameters in the model but also enhances its feature extraction capabilities. Additionally, we replace the ELAN module in the Neck with the DPCH-ELAN module and introduce the darknetblock module along with the pcconv convolution. These modifications improve the network's ability to comprehend complex patterns and semantics, thereby enabling it to capture features at different levels of input data. Moreover, we introduce the pcconv convolution building block at the output side to handle heterogeneous information in complex road scenes, thereby enhancing the network's performance in detecting road targets and abnormalities. In our experiments using a mul-ti-source dataset, the improved model exhibits a reduction in GFLOPs by 20.90% and a decrease in the number of pa-rameters by 24.49%. Furthermore, the mean average precision scores (map) at thresholds of 0.5 and 0.5~0.9 are im-proved to 77.3% and 51.8%, respectively, compared to the original YOLOv7-Tiny model. These experimental results demonstrate that our enhanced model achieves a reduction in model size while simultaneously enhancing detection accuracy, thereby meeting the requirements for real-time detection. To assess the generalizability of our approach, we conducted comparison experiments on the VOC2012 dataset. The results indicate that the improved algorithm exhibits robust generalization capabilities across different datasets.

Automated image clarity detection for the improvement of colposcopy imaging with multiple devices

The proportion of women dying from cervical cancer in middle- and low-income countries is over 60%, twice that of their high-income counterparts. A primary screening strategy to eliminate this burden is cervix visualization and application of 3–5% acetic acid, inducing contrast in potential lesions. Recently, machine learning tools have emerged to aid visual diagnosis. As low-cost visualization tools expand, it is important to maximize image quality at the time of the exam or of images used in algorithms. Objective: We present the use of an object detection algorithm, the YOLOv5 model, to localize the cervix and describe blur within a multi-device image database. Methods: We took advantage of the Fourier domain to provide pseudo-labeling of training and testing images. A YOLOv5 model was trained using Pocket Colposcope, Mobile ODT EVA, and standard of care digital colposcope images. Results: When tested on all devices, this model achieved a mean average precision score, sensitivity, and specificity of 0.9, 0.89, and 0.89, respectively. Mobile ODT EVA and Pocket Colposcope hold out sets yielded mAP scores of 0.81 and 0.83, respectively, reflecting the generalizability of the algorithm. Compared to physician annotation, it yielded an accuracy of 0.72. Conclusion: This method provides an informed quantitative, generalizable analysis of captured images that is highly concordant with expert annotation. Significance: This quality control framework can assist in the standardization of colposcopy workflow, data acquisition, and image analysis and in doing so increase the availability of usable positive images for the development of deep learning algorithms.

Hardware acceleration of Tiny YOLO deep neural networks for sign language recognition: A comprehensive performance analysis

In this paper, we benchmark two automation frameworks, Vitis AI and FINN, for sign language recognition on a Field Programmable Gate Array (FPGA). We conducted an in-depth exploration of both frameworks using Tiny YOLOv2 networks by varying design parameters such as precision, parallelism ratio, etc. Further, a fair baseline comparison is made based on accuracy, speed, and hardware resources. Experimental findings demonstrate that the Vitis AI outperforms the FINN framework and traditional GPU and CPU platforms by achieving significant improvements of 1.08x, 1.7x, and 2.9x in terms of latency. Leveraging Vitis AI, our system achieved a detection speed of 32.7 frames per second (FPS) on the Kria KV260 FPGA with a power consumption rate of 5.6 W and an impressive mean Average Precision (mAP) score of 61.2% on the Hindi Indian Sign Language (ISL) dataset.

Intelligent accounting optimization method based on meta-heuristic algorithm and CNN.

The evolution of social intelligence has led to the adoption of intelligent accounting practices in enterprises. To enhance the efficiency of enterprise accounting operations and improve the capabilities of accountants, we propose an intelligent accounting optimization approach that integrates meta-heuristic algorithms with convolutional neural networks (CNN). First, we enhance the CNN framework by incorporating document and voucher information into accounting audits, creating a multi-modal feature extraction mechanism. Utilizing these multi-modal accounting features, we then introduce a method for assessing accounting quality, which objectively evaluates financial performance. Finally, we propose an optimization technique based on meta-heuristic principles, combining genetic algorithms with annealing models to improve the accounting system. Experimental results validate our approach, demonstrating an accuracy of 0.943 and a mean average precision (mAP) score of 0.812. This method provides technological support for refining accounting audit mechanisms.

Session context data integration to address the cold start problem in e-commerce recommender systems

Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the ‘cold-start’ problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions’ contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.

YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection

During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection.

Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

AbstractLow‐light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low‐light object detection is presented using state‐of‐the‐art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low‐light conditions. The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low‐light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low‐light object detection, offering promising solutions for real‐world applications like nighttime surveillance and autonomous navigation in low‐light conditions, addressing the growing demand for advanced low‐light object detection.

Enhancing fruit and vegetable detection in unconstrained environment with a novel dataset

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to sustainable and technologically advanced farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, a dataset named FRUVEG67 was curated that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. A semi-supervised data annotation algorithm (SSDA) was developed that generates bounding boxes for objects to label the remaining nonannotated images. For detection, Fruit and Vegetable Detection Network (FVDNet) was proposed, an ensemble version of YOLOv8n featuring three distinct grid configurations. In addition, an averaging approach for the prediction of the bounding box and a voting mechanism for the prediction of the classes was implemented. Finally Jensen–Shannon Divergence (JSD) in conjunction with focal loss was integrated as the overall loss function for better detection of smaller objects. Experimental results highlight the superiority of FVDNet compared to recent versions of YOLO, showcasing remarkable improvements in detection and localization performance. An impressive mean average precision (mAP) score of 0.82 across all classes was achieved. Furthermore, the efficacy of FVDNet on open-category refrigerator images were evaluated, where it demonstrates promising results.

Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN

Counting planthoppers manually is laborious and yields inconsistent results, particularly when dealing with species with similar features, such as the brown planthopper (Nilaparvata lugens; BPH), whitebacked planthopper (Sogatella furcifera; WBPH), zigzag leafhopper (Maiestas dorsalis; ZIGZAG), and green leafhopper (Nephotettix malayanus and Nephotettix virescens; GLH). Most of the available automated counting methods are limited to populations of a small density and often do not consider those with a high density, which require more complex solutions due to overlapping objects. Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. It utilises annotated datasets obtained from sticky light traps, comprising 1654 images across four distinct classes of planthoppers and one class of benign insects. The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. The results indicated that Faster R-CNN VGG 16 outperformed other models, achieving a mean average precision (mAP) score of 97.69% and exhibiting exceptional accuracy in classifying all planthopper categories. The correctness of the model was verified by entomologists, who confirmed a classification and counting accuracy rate of 98.84%. Nevertheless, the model fails to recognise certain samples because of the high density of the population and the significant overlap among them. This research effectively resolved the issue of low- to medium-density samples by achieving very precise and rapid detection and counting.

AEPF: Attention-Enabled Point Fusion for 3D Object Detection.

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class's easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Bangladeshi crops leaf disease detection using YOLOv8

The agricultural sector in Bangladesh is a cornerstone of the nation's economy, with key crops such as rice, corn, wheat, potato, and tomato playing vital roles. However, these crops are highly vulnerable to various leaf diseases, which pose significant threats to crop yields and food security if not promptly addressed. Consequently, there is an urgent need for an automated system that can accurately identify and categorize leaf diseases, enabling early intervention and management. This study explores the efficacy of the latest state-of-the-art object detection model, YOLOv8 (You Only Look Once), in surpassing previous models for the automated detection and categorization of leaf diseases in these five major crops. By leveraging modern computer vision techniques, the goal is to enhance the efficiency of disease detection and management. A dataset comprising 19 classes, each with 150 images, totaling 2850 images, was meticulously curated and annotated for training and evaluation. The YOLOv8 framework, known for its capability to detect multiple objects simultaneously, was employed to train a deep neural network. The system's performance was evaluated using standard metrics such as mean Average Precision (mAP) and F1 score. The findings demonstrate that the YOLOv8 framework successfully identifies leaf diseases, achieving a high mAP of 98% and an F1 score of 97%. These results underscore the significant potential of this approach to enhance crop disease management, thereby improving food security and promoting agricultural sustainability in Bangladesh.

Segmenting hydrogen‐induced cracking defects in steel through scanning acoustic microscopy and deep neural networks

AbstractHydrogen‐induced cracking (HIC) presents a significant concern in industries, such as oil and gas, petrochemicals, and aerospace, where high‐strength steel is prevalently used. This phenomenon compromises the structural integrity of steel pipelines and equipment. Accurate detection and monitoring of HIC are crucial for the safety and reliability of these assets. While traditional defect detection methods are usually costly and easily affected by human physiological state, deep learning approaches offer both time and financial benefits along with high accuracy. This study focuses on employing deep learning to segment HIC in steel, utilizing the robust and state‐of‐the‐art YOLOv8‐seg architecture for defect identification and segmentation. The process involves acquiring two‐dimensional B‐scan images through a scanning acoustic microscopy (SAM) system, followed by employing the YOLOv8‐seg architecture to address the segmentation task within these images. The experimental results demonstrate the effectiveness of the YOLOv8‐seg model, achieving a mean average precision (mAP) score greater than ˜0.95. Notably, this research pioneers the development of a framework for reconstructing HIC within steel based on B‐scan image segmentation results, offering researchers and professionals a comprehensive understanding of internal defects in steel blocks. This work underscores the potential of YOLOv8‐seg architecture for accurate detection and segmentation of HIC in steel, providing a valuable tool for inspection and maintenance activities.

Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network

High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model’s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model’s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering.

Deep learning application of vertebral compression fracture detection using mask R-CNN

Vertebral compression fractures (VCFs) of the thoracolumbar spine are commonly caused by osteoporosis or result from traumatic events. Early diagnosis of vertebral compression fractures can prevent further damage to patients. When assessing these fractures, plain radiographs are used as the primary diagnostic modality. In this study, we developed a deep learning based fracture detection model that could be used as a tool for primary care in the orthopedic department. We constructed a VCF dataset using 487 lateral radiographs, which included 598 fractures in the L1-T11 vertebra. For detecting VCFs, Mask R-CNN model was trained and optimized, and was compared to three other popular models on instance segmentation, Cascade Mask R-CNN, YOLOACT, and YOLOv5. With Mask R-CNN we achieved highest mean average precision score of 0.58, and were able to locate each fracture pixel-wise. In addition, the model showed high overall sensitivity, specificity, and accuracy, indicating that it detected fractures accurately and without misdiagnosis. Our model can be a potential tool for detecting VCFs from a simple radiograph and assisting doctors in making appropriate decisions in initial diagnosis.