Single Shot Multibox Detector Research Articles

Real-Time People Counting System

This paper introduces an advanced system for real-time people counting, utilizing live video streams or IP cameras. The core of this system is the Single Shot Multibox Detector (SSD) framework, enhanced with MobileNet architecture to ensure precise object detection. To continuously track detected individuals, we employ a centroid tracking algorithm. The system's design prioritizes scalability, making it ideal for various business applications, such as monitoring foot traffic in stores, buildings, and shopping malls. A key feature of our system is its ability to send immediate alerts when the number of people exceeds a set threshold. This functionality is crucial for managing crowd control and adhering to safety protocols, particularly in scenarios like the COVID-19 pandemic. We have optimized the system’s performance with threading, which improves its responsiveness and efficiency. By integrating advanced machine learning techniques with practical business applications, our real-time people counting system offers a dependable and scalable solution for contemporary crowd management challenges. Features like real-time alerts, automatic scheduling, and comprehensive data logging highlight its effectiveness in enhancing operational efficiency and safety across various settings.

Deep learning models for vision-based occupancy detection in high occupancy buildings

Accurate occupancy information is crucial for enhancing energy efficiency and reducing carbon emissions in buildings. However, the inherent unpredictability of occupants introduces uncertainties in energy analysis and control strategy development. To address these challenges, this study proposes a vision-based method employing state-of-the-art deep learning models to capture real-time occupancy profiles in crowded indoor spaces. Utilising a self-collected image dataset, various deep learning models, including Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Networks (Faster R-CNN), and different versions of You Only Look Once (YOLO) were trained and evaluated. An experiment was conducted in a lecture room equipped with cameras and environmental sensors to evaluate the performance of each model in terms of precision, computational efficiency, and adaptability to varying occupancy levels during a lecture session. The session included varying occupancy conditions: entering (barely occupied), during the lecture (typical occupancy), and leaving the room (again barely occupied). Among the models tested, YOLOv8x exhibited the best performance in terms of accuracy, while SSD lagged notably. The impact on the detection performance of various locations of camera setups was also explored. Energy simulations revealed that deep learning-based model generated occupancy profiles significantly deviated from conventional “fixed” occupancy profiles, resulting in a 13.45 % variation in predicted heating energy demand. However, compared to the ground truth, these profiles showed minimal variation (up to 6.72 %) for the Faster R-CNN and YOLO models, highlighting their accuracy and robustness. Additionally, although the deep learning-based occupancy profiles generally overpredicted the recorded data, the CO2 concentration trends they predicted aligned closely with the recorded data, unlike the “fixed” occupancy profiles. The findings underscore the importance of realistic occupancy profiles for reliable energy predictions in buildings and demonstrate the potential of the proposed vision-based method for advancing occupancy detection and building energy management.

Comparison of CNN-Based Architectures for Detection of Different Object Classes

(1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. (4) Conclusion: Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1-0.88, and FPS-48, so the focus was on these models.

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.

Coupling design features of material surface treatment for ceramic products based on ResNet

Abstract Ceramic products is one of the important carriers of various civilizations, reflecting the lifestyle, aesthetic concepts, and technological level of society at that time. In order to study the surface treatment design features of ceramic craft products, this article analyzed the ceramic features through computer vision technology and used residual neural networks to detect the surface treatment features of ceramic craft products. The extracted texture features were classified to study and analyze the coupling features of different glazes, colors, and shapes on the formation of different textures. This study used ResNeXt50-SSD, which combined ResNeXt50 and SSD (Single Shot MultiBox Detector) algorithms, to compare feature detection with LeNet-5, VGG-16, and MobileNetV2 network models. From the experimental findings, it can be concluded that ResNeXt50-SSD was the most effective for feature recognition of ceramic craft products, with precision, recall, and mAP of 94.3, 92.1, and 89.5%, respectively. Therefore, the combination of ResNeXt50 and SSD algorithms is an effective method for detecting surface treatment features of ceramic craft products.

Documenting customary land boundaries using unmanned aerial vehicle imagery and artificial intelligence

AbstractThe use of computer vision and deep learning in boundary documentation for land registration stems from the ongoing demand for appropriate mapping approaches of unregistered land rights to eradicate the global challenge of tenure insecurity. Previous research has yielded promising results towards automated extraction of photo‐visible cadastral boundaries from high‐resolution imagery. Nonetheless, the extraction of invisible cadastral boundaries is still a challenge. This study investigates the place of sensor/s on‐board unmanned aerial vehicles and deep learning algorithms in detecting cadastral boundaries. It develops a participatory boundary marking procedure using low‐cost markers to bring monument to previously invisible and ill‐defined cadastral boundaries. After that, the researchers trained and tested the accuracy of a convolutional neural network, namely single shot multi‐box detector (SSD) based on Residual Neural Network (ResNet) and Visual Geometry Group (VGG) backbone networks to automatically detect cadastral boundary markers from unmanned aerial vehicle imagery. SSD based on ResNet34 performed best with 0.88 precision, 0.92 recall and 0.91 F measure or (F1) score. VGG19‐based SSD yielded a precision of 0.47, recall of 0.53 and F1 score of 0.50. The horizontal accuracy of the cadastral map generated varied from 0.089 to 0.496 m per parcel, with a standard deviation of 0.120 m. Results show that this approach is practical for cadastral mapping in rural areas.

Acoustic Imaging Learning-Based Approaches for Marine Litter Detection and Classification

This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments.

Survivor detection approach for post earthquake search and rescue missions based on deep learning inspired algorithms

Rapid and reliable detection of human survivors trapped under debris is crucial for effective post-earthquake search and rescue (SAR) operations. This paper presents a novel approach to survivor detection using a snake robot equipped with deep learning (DL) based object identification algorithms. We evaluated the performance of three main algorithms: Faster R-CNN, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO). While these algorithms are initially trained on the PASCAL VOC 2012 dataset for human identification, we address the lack of a dedicated dataset for trapped individuals by compiling a new dataset of 200 images that specifically depicts this scenario, featuring cluttered environment and occlusion. Our evaluation takes into account detection accuracy, confidence interval, and running time. The results demonstrate that the YOLOv10 algorithm achieves the 98.4 mAP@0.5, accuracy of 98.5% for inference time of 15 ms. We validate the performance of these algorithms using images of human survivors trapped under debris and subjected to various occlusions.

Detection Method for Bolt Missing Pins of Transmission Line Based on Improved SSD Network

The bolt pin structure plays a role in connecting and fixing various components in transmission lines, and the pins are prone to detachment, which may lead to the disintegration of key components and cause serious consequences. The bolt pin target size in the transmission line inspection image is small, the background is complex, and the detection effect using traditional object detection algorithms is poor. This article proposes a method for detecting missing bolts in transmission lines based on an improved SSD (Single Shot MultiBox Detector) network. Using the traditional SSD network as the foundation, a feature fusion module is first added to the network to enrich the semantic information of low-level feature maps and the detail information of high-level feature maps through the mutual fusion of high-level and low-level feature maps. Secondly, embedding a channel attention mechanism module enhances the channel features corresponding to the bolt pin structure and suppresses channel features that are irrelevant to the background; At the same time, the introduction of Focal Loss loss function reduces the unevenness of positive and negative sample classification. Finally, experiments were conducted on the collected bolt missing pin detection dataset, and the results showed that the method proposed in this paper has good performance in detecting missing pins, and the detection accuracy has been significantly improved.

Anomalous white shrimp detection in intensive farming based on improved YOLOv8

Timely detection of anomalous shrimp is crucial for ensuring farming safety. Therefore, this study developed an effective model to detect abnormal shrimp behaviors, including curling, floating, leaping, cannibalism, and death. The proposed model uses YOLOv8 as the baseline, adjusts network parameters to align with the characteristics of abnormal shrimp, employs content-aware reassembly of features (CARAFE) to preserve more semantic information, and utilizes dynamic convolution to enhance the network's expressiveness. Achieving a 97.8 % mAP@0.5 and 96.1 % F1 score on a custom dataset, the model demonstrated superior detection performance and a smaller size compared with Faster-RCNN, single-shot multi-box detector (SSD), YOLOv5, YOLOv6, and YOLOv7. Based on the proposed model, we developed an abnormal shrimp monitoring system with significant potential to benefit white shrimp cultivators.

Novel intelligent defects detection of boiler water walls in thermal power plants based on OFM_SSD

The boiler is a critical component of conventional thermal power systems, where surface flaws in boiler water walls can significantly compromise safety and availability, potentially leading to substantial loss of life and property. Traditional detection methods, whether manual or based on machine learning, often prove costly, inefficient and time-consuming, failing to meet the stringent requirements for water wall inspection. Therefore, a novel surface defect detection model integrating an improved single shot multibox detector (SSD) with the optical flow method (OFM) (OFM_SSD) is proposed. The OFM enhances data sample diversity by augmenting the dataset derived from thermal power plants, while the incorporation of deconvolution techniques improves the model receptive field, thereby enhancing its ability to detect and classify small defects. Comprehensive experiments demonstrate that the OFM_SSD outperforms several existing models including the SSD model based on traditional expanded datasets (T_SSD), you only look once (YOLO), ordinary SSD, Regions with the CNN(R_CNN), and Deconvolution-only SSD (DSSD) in terms of accuracy in defect localization and classification. This advancement of the OFM_SSD not only reduces operational costs but also enhances detection capabilities, ultimately contributing to safer and more efficient operations within thermal power plants.

Model optimization and acceleration method based on meta-learning and model pruning for laser vision weld tracking system

Purpose This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs. Design/methodology/approach The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy. Findings Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications. Originality/value This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

M‐SSD based on anchor proposal and ResNet101 backbone for placental haemorrhage MRI detection

AbstractMRI (magnetic resonance imaging) images can effectively show the placental haemorrhage area. In view of the special properties and real‐time detection requirements of placental haemorrhage MRI images, this paper has systematically improved the single‐shot multi‐box detector (SSD) target detection algorithm (M‐SSD). First, taking advantage of the particularity of the MRI image, the maximum stable extremum region (MSER) algorithm was used as the anchor proposal network which integrated the proposal information into the feature layer of SSD to avoid the hungry traversal of the original algorithm. Second, after the scale statistics of the placental haemorrhage area in MRI images, the bounding box matching the size of the placental haemorrhage area was redefined, in this way, the scale of the bounding box will have application pertinence, which can effectively improve the detection accuracy of the algorithm. Third, due to the small target property of the placental haemorrhage area in the MRI image, the VGG16 basic network in the original SSD was replaced by ResNet101, this made the algorithm have higher performance in small target detection. Finally, the Placental Haemorrhage MRI Detection Database (PHMD) has been built which is not only a base for this paper, but also for further research in this area.

Research on traffic sign detection algorithm based on improved SSD in complex environments

Abstract In complex traffic sign environments, detection challenges include uneven size distribution, insufficient valid information, and difficulties in identifying targets under resource constraints, leading to missed and false detections. This study proposes an enhanced lightweight traffic sign detection algorithm based on SSD (Single Shot MultiBox Detector). By replacing the original backbone network with MobileNetV2, the model is streamlined to have fewer parameters, which improves generalization in complex environments. This modification significantly boosts the recall rate, achieving a better balance between precision and recall. By introducing the FPN(Feature Pyramid Network) combined with the CBAM(Convolutional Block Attention Module) attention mechanism, the detailed and semantic information between deep and shallow layers is fully integrated, reducing the loss of feature information, thus enhancing the strengthening of key information of traffic signs and the adaptability to different scales of traffic signs. Finally, by integrating the cross-attention mechanism, the algorithm's anti-interference ability in complex environments is improved, and the positioning accuracy of traffic signs is enhanced by capturing the dependency between different positions. Through ablation experiments and comparative experiments on a public traffic sign dataset, our improved SSD algorithm achieved an mAP of 89.97%. Compared with the original algorithm, the mAP increased by 12.41%, the recall rate increased by 18.38%, and the sum of precision and recall F1 increased by 14.6%. These improvements significantly enhance the performance of traffic sign detection in complex environments, thereby meeting the performance requirements of traffic sign detection.

Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.

Innovative healthcare solutions: robust hand gesture recognition of daily life routines using 1D CNN.

Hand gestures are an effective communication tool that may convey a wealth of information in a variety of sectors, including medical and education. E-learning has grown significantly in the last several years and is now an essential resource for many businesses. Still, there has not been much research conducted on the use of hand gestures in e-learning. Similar to this, gestures are frequently used by medical professionals to help with diagnosis and treatment. We aim to improve the way instructors, students, and medical professionals receive information by introducing a dynamic method for hand gesture monitoring and recognition. Six modules make up our approach: video-to-frame conversion, preprocessing for quality enhancement, hand skeleton mapping with single shot multibox detector (SSMD) tracking, hand detection using background modeling and convolutional neural network (CNN) bounding box technique, feature extraction using point-based and full-hand coverage techniques, and optimization using a population-based incremental learning algorithm. Next, a 1D CNN classifier is used to identify hand motions. After a lot of trial and error, we were able to obtain a hand tracking accuracy of 83.71% and 85.71% over the Indian Sign Language and WLASL datasets, respectively. Our findings show how well our method works to recognize hand motions. Teachers, students, and medical professionals can all efficiently transmit and comprehend information by utilizing our suggested system. The obtained accuracy rates highlight how our method might improve communication and make information exchange easier in various domains.

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Despite advancements in technology, safety equipment, and training within the construction industry over recent decades, the prevalence of fatal and nonfatal injuries and accidents remains a significant concern among construction workers. Hard hats and safety vests are crucial safety gear known to mitigate severe head trauma and other injuries. However, adherence to safety protocols, including the use of such gear, is often inadequate, posing potential risks to workers. Moreover, current manual safety monitoring systems are laborious and time-consuming. To address these challenges and enhance workplace safety, there is a pressing need to automate safety monitoring processes economically, with reduced processing times. This research proposes a deep learning-based pipeline for real-time identification of non-compliance with wearing hard hats and safety vests, enabling safety officers to preempt hazards and mitigate risks at construction sites. We evaluate various neural networks for edge deployment and find that the Single Shot Multibox Detector (SSD) MobileNet V2 model excels in computational efficiency, making it particularly suitable for this application-oriented task. The experiments and comparative analyses demonstrate the pipeline’s effectiveness in accurately identifying instances of non-compliance across different scenarios, underscoring its potential for improving safety outcomes.

Recognition of Brazilian vertical traffic signs and lights from a car using Single Shot Multi box Detector

Recognition of Brazilian vertical traffic signs and lights from a car using Single Shot Multi box Detector

EDGE AI BASED OBJECT DETECTION SYSTEM USING TFLITE

Edge computing has gained prominence in recent years due to its ability to process data closer to the source, reducing latency and bandwidth requirements. In this paper, we propose an Edge AI Based Object Detection System using TensorFlow Lite (TFLite), designed to perform real-time object detection on resource-constrained edge devices. The system leverages the efficiency and portability of TFLite, a lightweight framework for deploying machine learning models on edge devices, to enable efficient inference without relying on cloud connectivity. Our proposed system integrates state-of-the-art object detection models, such as SSD (Single Shot Multibox Detector) and YOLO (You Only Look Once), into the TFLite runtime environment. Through optimization techniques such as model quantization, pruning, and architecture modifications, we tailor these models to meet the computational and memory constraints of edge devices while maintaining high detection accuracy. Furthermore, we explore hardware acceleration options, including GPU and DSP (Digital Signal Processor), to further enhance inference speed and energy efficiency. We evaluate the performance of the Edge AI Based Object Detection System on various edge devices, including smartphones, IoT (Internet of Things) devices, and embedded systems. Real-world deployment scenarios are considered, encompassing applications such as smart surveillance, industrial automation, and autonomous vehicles. The results demonstrate the system's ability to achieve real-time object detection with low latency and minimal resource consumption, making it well-suited for edge computing environments where real-time responsiveness and privacy are paramount concerns. Keywords: object Detection, Machine Learning, Tensor Flow lite, deep learning.

Detection of pulmonary nodules in chest radiographs: novel cost function for effective network training with purely synthesized datasets

PurposeMany large radiographic datasets of lung nodules are available, but the small and hard-to-detect nodules are rarely validated by computed tomography. Such difficult nodules are crucial for training nodule detection methods. This lack of difficult nodules for training can be addressed by artificial nodule synthesis algorithms, which can create artificially embedded nodules. This study aimed to develop and evaluate a novel cost function for training networks to detect such lesions. Embedding artificial lesions in healthy medical images is effective when positive cases are insufficient for network training. Although this approach provides both positive (lesion-embedded) images and the corresponding negative (lesion-free) images, no known methods effectively use these pairs for training. This paper presents a novel cost function for segmentation-based detection networks when positive–negative pairs are available.MethodsBased on the classic U-Net, new terms were added to the original Dice loss for reducing false positives and the contrastive learning of diseased regions in the image pairs. The experimental network was trained and evaluated, respectively, on 131,072 fully synthesized pairs of images simulating lung cancer and real chest X-ray images from the Japanese Society of Radiological Technology dataset.ResultsThe proposed method outperformed RetinaNet and a single-shot multibox detector. The sensitivities were 0.688 and 0.507 when the number of false positives per image was 0.2, respectively, with and without fine-tuning under the leave-one-case-out setting.ConclusionTo our knowledge, this is the first study in which a method for detecting pulmonary nodules in chest X-ray images was evaluated on a real clinical dataset after being trained on fully synthesized images. The synthesized dataset is available at https://zenodo.org/records/10648433.