Learning-based Object Detection Algorithm Research Articles

Deep-Learning-Based Anti-Collision System for Construction Equipment Operators

Due to the dynamic environment of construction sites, worker collisions and stray accidents caused by heavy equipment are constantly occurring. In this study, a deep learning-based anti-collision system was developed to improve the existing proximity warning systems and to monitor the surroundings in real time. The technology proposed in this paper consists of an AI monitor, an image collection camera, and an alarm device. The AI monitor has a built-in object detection algorithm, automatically detects the operator from the image input from the camera, and notifies the operator of a danger warning. The deep learning-based object detection algorithm was trained with an image data set composed of a total of 42,620 newly constructed in this study. The proposed technology was installed on an excavator, which is the main equipment operated at the construction site, and performance tests were performed, and it showed the potential to effectively prevent collision accidents.

Microorganism Detection in Activated Sludge Microscopic Images Using Improved YOLO

Wastewater has detrimental effects on the natural environment. The activated sludge method, a widely adopted approach for wastewater treatment, has proven highly effective. Within this process, microorganisms play a pivotal role, necessitating continuous monitoring of their quantity and diversity. Conventional methods, such as microscopic observation, are time-consuming. With the widespread integration of computer vision technologies into object detection, deep learning-based object detection algorithms, notably the You Only Look Once (YOLO) model, have garnered substantial interest for their speed and precision in detection tasks. In this research, we applied the YOLO model to detect microorganisms in microscopic images of activated sludge. Furthermore, addressing the irregular shapes of microorganisms, we developed an improved YOLO model by incorporating deformable convolutional networks and an attention mechanism to enhance its detection capabilities. We conducted training and testing using a custom dataset comprising five distinct objects. The performance evaluations used in this study utilized metrics such as the mean average precision at intersections over a union threshold of 0.5 (mAP@0.5), with the improved YOLO model achieving a mAP@0.5 value of 93.7%, signifying a 4.3% improvement over the YOLOv5 model. Comparative analysis of the improved YOLO model and other object detection algorithms on the same dataset revealed a higher accuracy for the improved YOLO model. These results demonstrate the superior performance of the improved YOLO model in the task of detecting microorganisms in activated sludge, providing an effective auxiliary method for wastewater treatment monitoring.

Shine: A deep learning-based accessible parking management system

The ongoing expansion of urban areas facilitated by advancements in science and technology has resulted in a considerable increase in the number of privately owned vehicles worldwide, including in South Korea. However, this gradual increment in the number of vehicles has inevitably led to parking-related issues, including the abuse of disabled parking spaces (hereafter referred to as accessible parking spaces) designated for individuals with disabilities. Traditional license plate recognition (LPR) systems have proven inefficient in addressing such a problem in real-time due to the high frame rate of surveillance cameras, the presence of natural and artificial noise, and variations in lighting and weather conditions that impede detection and recognition by these systems. With the growing concept of parking 4.0, many sensors, IoT and deep learning-based approaches have been applied to automatic LPR and parking management systems. Nonetheless, the studies show a need for a robust and efficient model for managing accessible parking spaces in South Korea. To address this, we have proposed a novel system called, ‘Shine’, which uses the deep learning-based object detection algorithm for detecting the vehicle, license plate, and disability badges (referred to as cards, badges, or access badges hereafter) and verifies the rights of the driver to use accessible parking spaces by coordinating with the central server. Our model, which achieves a mean average precision of 92.16%, is expected to address the issue of accessible parking space abuse and contributes significantly towards efficient and effective parking management in urban environments.

Autonomous dental treatment planning on panoramic x-ray using deep learning based object detection algorithm

Autonomous dental treatment planning on panoramic x-ray using deep learning based object detection algorithm

Improvement of Vehicle detection and classification performance with Region of Interest

This Abstract-Paper presents an innovative approach to improve the performance of vehicle detection and classification in the context of computer vision. This study proposes the use of Region of Interest (ROI) to identify and process vehicle information in traffic images. The proposed method is based on a combination of deep learning-based object detection algorithms with a more focused ROI-based approach. First, we use state-of-the-art convolution neural networks to perform object detection in traffic images, which involves a visual recognition process to identify vehicle locations. Then, using ROI, we reorder and reduce the dimensions of irrelevant data to remove background areas and increase attention to vehicle objects. This aims to reduce the computational load and speed up the detection and classification processes. We evaluate the proposed approach on a wide traffic dataset and compare the results with conventional object detection approaches without using ROI. The experimental results show that the proposed method outperforms the conventional approach in terms of vehicle detection speed and accuracy. In conclusion, this study succeeded in improving the performance of vehicle detection and classification by leveraging Region of Interest, which allows better focus on relevant regions and reduces computational complexity. These results can make an important contribution to the development of a more efficient and reliable traffic control and transportation system.

Lightweight object detection algorithm for robots with improved YOLOv5

Robot object detection is important for the realisation of robot intelligence. Currently, deep learning-based object detection algorithms are used for robotic object detection. However, it faces some challenges in practical applications, such as the fact that robots frequently use resource-constrained devices, resulting in detection algorithms with long computation times and undesired detection rates. In order to address these concerns, this paper proposes a lightweight object detection algorithm for robots with an improved YOLOv5. To reduce the amount of processing required for feature extraction and increase the speed of detection, the C3Ghost and GhostConv modules have been introduced into the YOLOv5 backbone. The DWConv module was used in conjunction with the C3Ghost module in the YOLOv5 neck network to further reduce the number of model parameters and maintain accuracy. The CA (Coordinated Attention) module is also introduced to improve the extraction of features from detected objects and suppress irrelevant features, thus improving the algorithm’s detection accuracy. To verify the performance of the method, we tested it with a self-built dataset (4561 robot images in total) and the PascalVOC dataset respectively. The results show that compared with the YOLOv5s on the self-built dataset, the algorithm has a 54% decrease in FLOPs and a 52.53% decrease in the number of model parameters without a decrease in mAP (0.5). The effectiveness and superiority of the algorithm is demonstrated through case studies and comparisons.

Multiple face mask wearer detection based on YOLOv3 approach

<div align="left"><a name="_Hlk108683337"></a><span lang="EN-US">The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. </span></div>

A lightweight object detection network in low-light conditions based on depthwise separable pyramid network and attention mechanism on embedded platforms

Deep learning-based object detection algorithms have been widely used and are effective in autonomous driving. But their performance degrades dramatically under low-light conditions. The existing mitigation approach for low-light object detection uses image enhancement preprocessing to improve detection performance with limited success. In addition, this preprocessing approach incurs extra computation costs, consumes more resources, and makes it more challenging to implement the algorithm on embedded platforms. This paper proposes a novel object detection network in low-light conditions for embedded platforms. The new system consists of two innovative modules, including a lightweight low-light enhancement network DS-PyLENet and an anchor-free lightweight object detector CFEDet. DS-PyLENet is a three-level pyramid network configured with depthwise separable convolution residual blocks (DSCRBs) and multiscale DSCRBs. The CFEDet is configured with an improved EfficientNet backbone network with Coordinate Attention (CA) and vanilla MBconv, i.e., CA-fused EfficientNet, an improved Path Aggregation Network (PAN) fusion module, and an anchor-free Global Focal Loss (GFL) detection head. Two evaluation metrics, relative recall ratios, are proposed to depict the effectiveness of image enhancement on object detection more intuitively. The new model is demonstrated to be superior to the assembled model of EnlightenGAN and YOLOv5n in terms of detection accuracy and speed on GPU and embedded platforms. The testing results of the new model reveal that 83.5% mAP is achieved on the Exdark dataset and 67.4% mAP on the DarkFace dataset. The running rates of GTX 1080Ti and NVIDIA Jetson Xavier NX are 22 FPS and 2.6 FPS, respectively. The recall of CFEDet with DS-PyLENet increased to 22.1% and significantly improved over 18% with Zero-DCE and 17.2% with EnlightenGAN.

Detection of sperm cells by single-stage and two-stage deep object detectors

Today, infertility is a common health concern that affects approximately 15%–20% of the world population. The evaluation of male patients with infertility includes diverse examination and laboratory tests in comparison to female patients. In the evaluation of male infertility, sperm specimens are examined in terms of morphometry, concentration, and motility. Detection of sperm is the critical step to determine the concentration and motility parameters. In this study, a fusion approach of deep learning-based object detection techniques is utilized in the sperm detection problem for obtaining more accurate and consistent concentration and motility characteristics of sperm specimens. First, 12 sperm specimen videos acquired from different infertile patients were recorded. Then, regions in the video frames were labeled as sperm and non-sperm by the experts. Two different scenarios have been tested over the labeled dataset. Differently arranged train and test data ratios were also utilized for each scenario. In the first scenario, deep learning-based object detection algorithms were individually performed for the sperm detection of patient-oriented videos in terms of different train/test split ratios. Videos with the lowest mAP (mean Average Precision) values resulted in the first scenario were selected for the second scenario as target videos. The rest of the videos were used for model training without any patient-oriented manner. In the second scenario, the detection performance for more challenging videos is aimed to increase by using different videos instead of using the small part of the same video in the model training. Additionally, a fusion idea of the utilized deep learning-based techniques was proposed and performed over these low mAP resulted videos to increase the detection performance. The results are compared regarding the general mAP, class-wise APs, and training time. In the first scenario, YOLOv5 achieved the best results, while the proposed fusion approach achieved the best mAP scores in the second scenario.

Non-destructive robotic sorting of cracked pistachio using deep learning

Pistachio, an agricultural product, is considered one of the guilt-free snacks due to its nutritional content, taste, and its health benefits. Usually, when it is consumed as a snack, in-shell pistachios are more desirable, whereas pistachios without their shell are ordinarily used for food preparation. One parameter important for pistachios sold as a snack is whether their shell is open. In pistachio processing plants, closed-shelled pistachios are separated with manual labor. There also exists an automated method called pin-picker machines, but as these machines harm pistachio kernels, they are undesirable. This paper offers a method to separate open-shelled pistachios from closed-shelled pistachios in an unharmful method. This method is done by using a deep learning-based object detection algorithm. In order to sort pistachios physically, a gripper and a conveyor system equipped Cartesian manipulator has been developed. In addition, a double-camera configuration is used. The key function of a double-camera configuration is to see the one-sided split on pistachios, which is hard to achieve with a single-camera configuration. Firstly, the object detection algorithm detects pistachios and gives their position with bounding boxes. Then their real-world coordinates are calculated, pistachios in both images taken with the cameras are paired, and the Cartesian manipulator separates pistachios. The detection accuracy was found as 98 % and 85 % for open-shelled pistachio and closed-shelled pistachio, respectively. With this method, closed-shelled pistachios can be sent to a cracking machine, or they can be sent to be stored for later use as they can withstand environmental effects better than open-shelled pistachios.

Multi-Scale Vision Transformer for Defect Object Detection

Defect object detection for printed circuit boards (PCBs) is one of the most important tasks in the field of industrial inspections. For this task, deep learning-based object detection algorithms are generally used. Regardless of whether one-stage or two-stage object detection algorithms are used, image features are extracted using multi-layer convolutional neural networks, which makes it hard to focus on global features. Few methods use Transformer-based models to extract features because of their computational complexity, which is proportional to the square of the length of the input, and they also lack the ability to extract multi-scale features. In our paper, we design a variant of the Vision Transformer (ViT) to extract features at multiple scales. We use a multi-stage ViT to extract image features, and then 1 × 1 convolution is used to connect the adjacent stages to enhance the feature dimensionality. Although the conventional ViT does not have the ability to extract multi-scale features, we use convolution to downsample the output of each stage of the ViT to obtain multi-scale features of the image. The size and stride of the convolution kernel used to downsample also change as the feature dimensionality changes. In addition, to reduce the computational complexity, we introduce window attention into each stage of the ViT, and as the feature dimensionality changes, the window size also changes. We construct a feature pyramid network and a path aggregation network based on the multi-scale features obtained as described above, and we adopt the detection head from YOLOx to implement the final multi-scale ViT-based object detection algorithm. Experimental results show that our improved model achieves a mean average precision of 69.3% on the MSCOCO 2017 test dataset, 86.0% on the PASCAL VOC 2007 test dataset, and 99.9% on our PCB ultrasonic image dataset.

Automatic Cropping of Handwritten Scanned Documents with Object Detection Algorithm

Automated cropping of handwritten letter fragments with the deep learning-based object detection algorithms is the current trend with an additional technique for the handling of handwritten text recognition with low cost and high speed. Handwriting is utilized to give access to people to do the extra analysis so it is required to be optically scanned as well as a machine familiar method. Owing to unhampered writing ways crosswise within linked and overlying characters, handwriting recognition endures an inspiring job. In this work, the technique is proposed for automatic cropping of handwritten letters from the image of the scanned document using the YOLOv3 algorithm for the detection in the supervision of XML files that contain the corner values of the images which are further learned by the CNN used to generate the bounding boxes. This method is an initial step in surveying the competency of diverse object detection methods in cropping handwritten letters from the scanned credentials. The Punjabi language handwritten dataset has 24 scanned documents used for training and 6 testing images are taken. The training is done on 267 letters and tested on 29 letters. The performance of the method shows the improvement in the accuracy of detection as well as facilitates the process of detection that leading to a reduction in time and costs. The 98.77% accuracy is achieved using the proposed technique which is better than state of art methods.

Social distance and face mask detector system exploiting transfer learning

<p class="IJASEITAbtract"><span lang="EN-US">As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.</span></p>

A Universal Landslide Detection Method in Optical Remote Sensing Images Based on Improved YOLOX

Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX (You Only Look Once) target detection model to address the poor detection of complex mixed landslides. Wherein the VariFocal is used to replace the binary cross entropy in the original classification loss function to solve the uneven distribution of landslide samples and improve the detection recall; the coordinate attention (CA) mechanism is added to enhance the detection accuracy. Firstly, 1200 historical landslide optical remote sensing images in thirty-eight areas of China were extracted from Google Earth to create a mixed sample set for landslide detection. Next, the three attention mechanisms were compared to form the YOLOX-Pro model. Then, we tested the performance of YOLOX-Pro by comparing it with four models: YOLOX, YOLOv5, Faster R-CNN, and Single Shot MultiBox Detector (SSD). The results show that the YOLOX-Pro(m) has significantly improved the detection accuracy of complex and small landslides than the other models, with an average precision (AP0.75) of 51.5%, APsmall of 36.50%, and ARsmall of 49.50%. In addition, optical remote sensing images of a 12.32 km2 group-occurring landslides area located in Mibei village, Longchuan County, Guangdong, China, and 750 Unmanned Aerial Vehicle (UAV) images collected from the Internet were also used for landslide detection. The research results proved that the proposed method has strong generalization and good detection performance for many types of landslides, which provide a technical reference for the broad application of landslide detection using UAV.

Analysis of Particle Size Distribution of Coke on Blast Furnace Belt Using Object Detection

Particle size distribution is an important parameter of metallurgical coke for use in blast furnaces. It is usually analyzed by traditional sieving methods, which cause delays and require maintenance. In this paper, a coke particle detection model was developed using a deep learning-based object detection algorithm (YOLOv3). The results were used to estimate the particle size distribution by a statistical method. Images of coke on the main conveyor belt of a blast furnace were acquired for model training and testing, and the particle size distribution determined by sieving was used for verification of the results. The experiment results show that the particle detection model is fast and has a high accuracy; the absolute error of the particle size distribution between the detection method and the sieving method was less than 5%. The detection method provides a new approach for fast analysis of particle size distributions from images and holds promise for a future online application in the plant.

Synthetic Image Dataset Development for Vision-Based Construction Equipment Detection

This paper presents a systematic method to create universally applicable synthetic training image datasets for computer vision-based construction object detection. The synthetic images created by inserting a virtual object of interest into a real site image allows us to minimize the time and effort for training image data collection and annotation. In addition, the use of synthetic images has an additional benefit that training images can be easily customized for a target construction site by considering the context of the site (e.g., different background scenes, camera positions, and angles) and the possible variability of target objects to be detected (e.g., different sizes, locations, rotation angles, and postures) on images. An automated approach proposed in this study attempts to systematically create the synthetic images using the Unity game engine in which context- and variability-related parameters can be controlled. The proposed method was validated by training a deep learning-based object detection algorithm [i.e., a faster regions with convolutional neural network (R-CNN) model] with synthetic images and testing it on real images from earthwork construction sites to detect an excavator. The CNN models trained with synthetic images showed an average precision value of more than 90%; in particular, the classifier using synthetic images outperformed the one using real site images. The detection results also demonstrated an improved capability to capture the high irregularity of a construction object on images when using techniques of context customization and variability randomization. The findings from this study demonstrate the feasibility and practicality of the use of synthetic images for vision-based approaches in a construction domain. Ultimately, the proposed approach serves as an alternative way to build comprehensive image datasets for construction entities, contributing to facilitating vision-based studies on construction.

SODA: A large-scale open site object detection dataset for deep learning in construction

Comprehensive image datasets can benefit the construction industry in terms of serving as the basis for generating deep-learning-based object detection models and testing the performance of object detection algorithms, but building such datasets is complex and requires vast professional knowledge. This paper develops and publicly releases a new large-scale image dataset specifically collected and annotated for the construction site, called Site Object Detection Dataset (SODA), which contains 15 object classes categorized by the worker, material, machine, and layout. >20,000 images were collected from multiple construction sites in different situations, weather conditions, and construction phases, covering different angles and perspectives. Statistical analysis shows that the dataset is well developed in terms of diversity and volume. Further evaluation with two widely-adopted deep learning-based object detection algorithms also illustrates the feasibility of the dataset, achieving a maximum mAP of 81.47%. This research contributes a large-scale open image dataset for the construction industry and sets up a performance benchmark for further evaluation of relevant algorithms.

Indigenous design of a Traffic Light Control system responsive to the local traffic dynamics and priority vehicles

Automation of traffic light control (TLC) systems is the major focus area compliant with Industry 4.0. The majority of the adaptive TLC systems are designed for homogeneous traffic of four-wheeler vehicles and are responsive to the prevailing traffic conditions at a junction. Further, they do not serve priority vehicles such as public transport (buses) or emergency vehicles (ambulance, fire trucks, etc.). The traffic dynamics considered in this work are less-lane-disciplined, heterogeneous traffic (two-three-four-wheeler vehicles), as found in the majority of developing countries. This work proposes a novel adaptive TLC system for heterogeneous traffic scenarios which is responsive to the prevailing traffic conditions at a junction, and also serves priority vehicles at a preference than the regular traffic. The proposed system supports multiple levels of priority among the priority vehicles, and efficiently schedules traffic lights at a junction to cause minimal interruption to the other regular traffic.For heterogeneous traffic monitoring, we use the Passenger Car Unit (PCU) count of the vehicles, which are computed using computer vision-based object detection models. The performance of deep learning-based object detection algorithms such as YOLO, RCNN, is analyzed experimentally for the computation requirements, and accuracy in computing PCU count. Simulations are carried out to analyze the effect of varying error rates in PCU count, and congestion level on the performance of the proposed adaptive TLC. The effect of the proposed adaptive TLC on the average travel and waiting times, of the priority vehicles and the other regular traffic is assessed. The simulation results suggest that the proposed algorithm can tolerate 20% error in the PCU count without degrading the performance. Additionally, this work demonstrates that the traffic information with the required accuracy can be processed in real-time using the available micro-controllers (e.g., Raspberry Pi or Jetson Nano).

딥러닝 기반 객체 탐지 결과의 불확실성을 고려한 자동 선박 탐지 및 추적

Recently, autonomous surface vehicles (ASVs) have attracted much research attention because of their potential effectiveness in carrying out various maritime missions such as surveillance and environmental monitoring. Situation awareness is a critical ability, and the camera is an essential sensor for ASVs in conducting such missions by automatically detecting and tracking objects in the surrounding environment. We detect and track the objects robustly by extracting the detection uncertainty and applying it to the tracking process. This study addresses automatic ship detection and tracking, considering the uncertainty of deep learning-based object detection using an onboard monocular camera. Gaussian YOLOv3, a deep learning-based object detection algorithm, is applied to detect a ship and estimate the uncertainty of the detection result. The detection results are sampled with uncertainty and propagated based on the camera geometry. Finally, the position of the target ship is estimated by a particle filter using the sampled detection results as measurements.

Deep Learning Approach to Nailfold Capillaroscopy Based Diabetes Mellitus Detection

Diabetes mellitus is a commonly occurring chronic metabolic disorder which has affected almost 400 million people around the world. It can lead to vascular structure alterations and various renal, cardiovascular, and neurologicalcomplications claiming several lives. Since diabetes mellitus results is vascular structure changes, NailfoldCapillaroscopy(NFC) based approach can be employed for the detection of diabetes. NFC is an inexpensive, non-invasive method which involves acquisition of images of capillaries in the nail bed region using a USB digital microscope. Qualitative parameters of the capillaries such as tortuosity, hemorrhages, angiogenesis, elongated capillariesand quantitative parameters like length, width and mean capillary density are considered for diabetes detection. About 600 capillary images of healthy and diabetic subjects were collected and further data augmentation was performed to increase this to 1018 images dataset. This paper focuses on using NFC to obtain capillary images and employdeep learning-based object detection algorithm to localize these capillary loops on the nailbed and differentiate them into five classes namely, normal, wide, elongated, tortuosity and hemorrhages. This classification is of prominent significance to medical practitioners as this helps in gauging the severity and progressionof the disorder.