Deep Learning Object Detection Algorithm Research Articles

Dynamic simultaneous localization and mapping based on object tracking in occluded environment

Abstract In practical applications, many robots equipped with embedded devices have limited computing capabilities. These limitations often hinder the performance of existing dynamic SLAM algorithms, especially when faced with occlusions or processor constraints. Such challenges lead to subpar positioning accuracy and efficiency. This paper introduces a novel lightweight dynamic SLAM algorithm designed primarily to mitigate the interference caused by moving object occlusions. Our proposed approach combines a deep learning object detection algorithm with a Kalman filter. This combination offers prior information about dynamic objects for each SLAM algorithm frame. Leveraging geometric techniques like RANSAC and the epipolar constraint, our method filters out dynamic feature points, focuses on static feature points for pose determination, and enhances the SLAM algorithm’s robustness in dynamic environments. We conducted experimental validations on the TUM public dataset, which demonstrated that our approach elevates positioning accuracy by approximately 54% and boosts the running speed by 75.47% in dynamic scenes.

Design of Fast Radio Burst Signal Recognition System based on Deep Learning

In order to identify fast radio burst signals from the original observation data of FAST radio telescopes, this paper designs a fast radio burst signal recognition system based on deep learning object detection algorithm. The system uses the incoherent achromatization algorithm and the YOLO series target recognition algorithm to realize the recognition of fast radio burst signals, and provides users with a friendly graphical system interface. In view of the different performance of users' computers, the system has the function of selecting different algorithm models. Experiments have proved that the system achieves 86% recall and 83% accuracy in the FRB20201124A real-world data test set.

Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings

Studying marine soundscapes by detecting known sound events and quantifying their spatio-temporal patterns can provide ecologically relevant information. However, the exploration of underwater sound data to find and identify possible sound events of interest can be highly time-intensive for human analysts. To speed up this process, we propose a novel methodology that first detects all the potentially relevant acoustic events and then clusters them in an unsupervised way prior to manual revision. We demonstrate its applicability on a short deployment. To detect acoustic events, a deep learning object detection algorithm from computer vision (YOLOv8) is re-trained to detect any (short) acoustic event. This is done by converting the audio to spectrograms using sliding windows longer than the expected sound events of interest. The model detects any event present on that window and provides their time and frequency limits. With this approach, multiple events happening simultaneously can be detected. To further explore the possibilities to limit the human input needed to create the annotations to train the model, we propose an active learning approach to select the most informative audio files in an iterative manner for subsequent manual annotation. The obtained detection models are trained and tested on a dataset from the Belgian Part of the North Sea, and then further evaluated for robustness on a freshwater dataset from major European rivers. The proposed active learning approach outperforms the random selection of files, both in the marine and the freshwater datasets. Once the events are detected, they are converted to an embedded feature space using the BioLingual model, which is trained to classify different (biological) sounds. The obtained representations are then clustered in an unsupervised way, obtaining different sound classes. These classes are then manually revised. This method can be applied to unseen data as a tool to help bioacousticians identify recurrent sounds and save time when studying their spatio-temporal patterns. This reduces the time researchers need to go through long acoustic recordings and allows to conduct a more targeted analysis. It also provides a framework to monitor soundscapes regardless of whether the sound sources are known or not.

Statistical analysis of metastable pitting behavior of 2024 aluminum alloy based on deep learning

In this study, a processing flow model for recognizing, statistically analyzing, and assessing pitting images in 2024 aluminum alloy was established based on deep learning object detection algorithms. By analyzing the statistical outcomes, the process of pitting development can be intuitively distinguished. Furthermore, the critical size for the transition from metastable pitting to stable pitting in aluminum alloy was accurately determined. In addition, by combining X-ray computed tomography testing, the evolution of pitting in aluminum alloys and its correlation with microstructure were delineated. Integrating deep learning into analysis provides valuable insights into the behavior and mechanisms of metastable pitting.

Development of a flexible endoscopic robot with autonomous tracking control ability using machine vision and deep learning

Abstract. A flexible endoscopic robot is designed to solve the problem that it is difficult for auxiliary doctors to maintain a stable visual field in traditional endoscopic surgery. Based on geometric derivation, a motion control method under the constraint of the remote center motion (RCM) of the robot system is established, and a set of circular trajectories are planned for it. The RCM error of the robot during operation and the actual trajectory of the robot end in three-dimensional space are obtained through the motion capture system. The end of the robot is controlled by the heterogeneous primary–secondary teleoperation control algorithm based on position increments. Finally, the RTMDet deep learning object detection algorithm was selected to identify and locate surgical instruments through comparative experiments, and the autonomous tracking control was completed based on visual guidance. In the process of autonomous tracking, the RCM error was less than 1 mm, which met the actual surgical requirements.

You only look once model-based object identification in computer vision

<span lang="EN-US">You only look once version 4 (YOLOv4) is a deep-learning object detection algorithm. It is used to decrease parameters and simplify network structures, making it suited for mobile and embedded device development. The YOLO detector can foresee an object's Class, bounding box, and probability of that Object's Class being found inside that bounding box. A probability value for each bounding box represents the likelihood of a given item class in that bounding box. Global features, channel attention, and special attention are also applied to extract more compelling information. Finally, the model combines the auxiliary and backbone networks to create the YOLOv4's entire network topology. Using custom functions developed upon YOLOv4, we get the count of the objects and a crop around the objects detected with a confidence score that specifies the probability of the thing seen being the same Class as predicted by YOLOv4. A confidence threshold is implemented to eliminate the detections with low confidence. </span>

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the “you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm.” Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of-the-art approaches in terms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5.

Telecommunication Fiber Box Detection Using YOLO in Urban Environment

The Fiber Distribution Panel (FDP) box is an essential piece of internet access hardware because it provides users with high-speed data networking and functions as a cable organizer to reduce wire clutter. After installing the FDP, an inspection must be performed to ensure that all necessary components are present. However, This examination is still done manually; the technician snaps a picture of the panel and sends it to its supervisor for verification, which is time-consuming and often prone to errors. In addition to images captured in low-light and complex environments, it makes it more difficult for humans to identify the components with just a naked eye. On this matter, a much more efficient method to assess the FDP installation work is very much needed. Therefore, using computer vision approaches, we utilize a deep learning algorithm to perform object detection and automate the assessment of FDP installation components based on visual data. One of the deep learning models established in the literature is the You Only Look Once (YOLO) model, a one-stage deep learning object detection algorithm that employs a fully conventional approach to generate highly accurate real-time predictions. This paper uses YOLOv5s to identify the fiber box and its relevant components, even in urban environments. Experimentations show that YOLO successfully identified the installation parts with a mean average precision score of 86% at a 0.5 confidence level, even with limited data.

Telecommunication Fiber Box Detection Using YOLO in Urban Environment

The Fiber Distribution Panel (FDP) box is an essential piece of internet access hardware because it provides users with high-speed data networking and functions as a cable organizer to reduce wire clutter. After installing the FDP, an inspection must be performed to ensure that all necessary components are present. However, This examination is still done manually; the technician snaps a picture of the panel and sends it to its supervisor for verification, which is time-consuming and often prone to errors. In addition to images captured in low-light and complex environments, it makes it more difficult for humans to identify the components with just a naked eye. On this matter, a much more efficient method to assess the FDP installation work is very much needed. Therefore, using computer vision approaches, we utilize a deep learning algorithm to perform object detection and automate the assessment of FDP installation components based on visual data. One of the deep learning models established in the literature is the You Only Look Once (YOLO) model, a one-stage deep learning object detection algorithm that employs a fully conventional approach to generate highly accurate real-time predictions. This paper uses YOLOv5s to identify the fiber box and its relevant components, even in urban environments. Experimentations show that YOLO successfully identified the installation parts with a mean average precision score of 86% at a 0.5 confidence level, even with limited data.

Multi-Cat Monitoring System Based on Concept Drift Adaptive Machine Learning Architecture

In multi-cat households, monitoring individual cats’ various behaviors is essential for diagnosing their health and ensuring their well-being. This study focuses on the defecation and urination activities of cats, and introduces an adaptive cat identification architecture based on deep learning (DL) and machine learning (ML) methods. The architecture comprises an object detector and a classification module, with the primary focus on the design of the classification component. The DL object detection algorithm, YOLOv4, is used for the cat object detector, with the convolutional neural network, EfficientNetV2, serving as the backbone for our feature extractor in identity classification with several ML classifiers. Additionally, to address changes in cat composition and individual cat appearances in multi-cat households, we propose an adaptive concept drift approach involving retraining the classification module. To support our research, we compile a comprehensive cat body dataset comprising 8934 images of 36 cats. After a rigorous evaluation of different combinations of DL models and classifiers, we find that the support vector machine (SVM) classifier yields the best performance, achieving an impressive identification accuracy of 94.53%. This outstanding result underscores the effectiveness of the system in accurately identifying cats.

Review of Object Detection Algorithms for Sonar Images based on Deep Learning

Background: Deep learning object detection algorithm is widely used in the field of image classification and has become an indispensable part. With the improvement of image classification accuracy, sonar image target detection algorithm based on deep learning has gradually become the focus of more and more people's research. Objective: This article aims to provide a summary and analysis of deep learning-based sonar image object detection algorithms, with the hope of offering insights for future research in the field of sonar target detection technology. Method: This paper systematically summarizes sonar image target detection algorithms based on deep learning. According to the method principle, the existing deep learning target detection algorithms are divided into four categories: target detection algorithm based on candidate region, deep target detection method based on regression, Anchor Free deep learning target detection algorithm, and search-based target detection and recognition algorithm. Then, the performance of algorithms based on COCO data sets is compared, and the standard sonar data sets and formats are introduced. Results: The sonar image object detection algorithm based on deep learning has made significant progress. The combination of deep learning and object detection methods has been applied to sonar images, resulting in the emergence of excellent performing algorithms. However, most algorithms are still in the developmental stage and face challenges in practical applications. Subsequently, several invention patents have been developed based on the aforementioned algorithms, including a feature extraction method for side-scan sonar images based on fully convolutional neural networks, an underwater sonar image target detection method based on improved YOLOv3-tiny, and more. Conclusion: Sonar image object detection technology based on deep learning has a wide range of application needs but also faces many difficulties and challenges, we still need to continue to learn and explore in future research, and we believe that we can make greater breakthroughs in the future.

A Task-Risk Consistency Object Detection Framework Based on Deep Reinforcement Learning

A discernible gap has materialized between the expectations for object detection tasks in optical remote sensing images and the increasingly sophisticated design methods. The flexibility of deep learning object detection algorithms allows the selection and combination of multiple basic structures and model sizes, but this selection process relies heavily on human experience and lacks reliability when faced with special scenarios or extreme data distribution. To address these inherent challenges, this study proposes an approach that leverages deep reinforcement learning within the framework of vision tasks. This study introduces a Task-Risk Consistent Intelligent Detection Framework (TRC-ODF) for object detection in optical remote sensing images. The proposed framework designs a model optimization strategy based on deep reinforcement learning that systematically integrates the available information from images and vision processes. The core of the reinforcement learning agent is the proposed task-risk consistency reward mechanism, which is the driving force behind the optimal prediction allocation in the decision-making process. To verify the effectiveness of the proposed framework, multiple sets of empirical evaluations are conducted on representative optical remote sensing image datasets: RSOD, NWPU VHR-10, and DIOR. When applying the proposed framework to representative advanced detection models, the mean average precision (mAP@0.5 and mAP@0.5:0.95) is improved by 0.8–5.4 and 0.4–2.7, respectively. The obtained results showcase the considerable promise and potential of the TRC-ODF framework to address the challenges associated with object detection in optical remote sensing images.

Advancing Plastic Waste Classification and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms

Plastics, with their versatility and cost-effectiveness, have become indispensable materials across various industries. However, the improper disposal and mismanagement of plastic waste have led to significant environmental issues, including pollution, habitat destruction, and threats to wildlife. To address these challenges, numerous methods for plastic waste sorting and recycling have been developed. While conventional techniques like near-infrared spectroscopy (NIRS) have been effective to some extent, they face difficulties in accurately classifying chemically similar samples, such as polyethylene terephthalate (PET) and PET-glycol (PET-G), which have similar chemical compositions but distinct physical characteristics. This paper introduces an approach that adapts image sensors and deep learning object detection algorithms; specifically, the You Only Look Once (YOLO) model, to enhance plastic waste classification based on the shape of the waste. Unlike conventional methods that rely solely on spectral analysis, our methodology aims to significantly improve the accuracy and efficiency of classifying plastics, especially when dealing with materials having similar chemical compositions but differing physical attributes. The system developed using image sensors and the YOLO model proves to be not only effective but also scalable and adaptable for various industrial and environmental applications. In our experiments, the results are strikingly effective. We achieved a classification accuracy rate exceeding 91.7% mean Average Precision (mAP) in distinguishing between PET and PET-G, surpassing conventional techniques by a considerable margin. The implications of this research extend far and wide. By enhancing the accuracy of plastic waste sorting and reducing misclassification rates, we can significantly boost recycling efficiency. The proposed approach contributes to a more sustainable and efficient plastic waste management system, alleviating the strain on landfills and mitigating the environmental impact of plastic waste, contributing to a cleaner and more sustainable environment.

Automated identification and counting of predated Ephestia kuehniella (Zeller) eggs using deep learning image analysis

Predation or kill rate of biological control agents is often used as a proxy to evaluate the efficacy of different species of natural enemies and under different conditions. For generalist predators and many egg parasitoids, eggs of the Mediterranean flour moth Ephestia kuehniella (Zeller) (Lepidoptera: Pyralidae) are used as factitious prey or hosts in laboratory and field experiments, as they are widely available from mass rearings of the biological control industry. Evaluating the predation or parasitism activity of natural enemies on E. kuehniella is a valuable tool for biological control practitioners around the world. However, manual assessments are laborious and prone to error, as observations may be subjective and depend on the individual performing the task. Here, we developed an automated protocol based on the deep learning object detection algorithm YOLOv5, for the accurate estimation of predated E. kuehniella eggs by piercing-sucking generalist predators. The application of the trained deep learning model achieved high precision (0.90) and recall (0.93) in the identification of predated eggs among intact eggs in our test experiment and was more accurate and faster than two independent observers that performed manual counting under a stereo microscope based on the mean absolute percentage error metric. Furthermore, a case study is presented where the predation activity of the generalist predators Orius laevigatus, Orius majusculus, Orius minutus, Nesidiocoris tenuis, Macrolophus pygmaeus, and Dicyphus errans is compared. Further applications and expansions of the protocol are discussed.

Extracting Masks from Herbarium Specimen Images Based on Object Detection and Image Segmentation Techniques

Extracting Masks from Herbarium Specimen Images Based on Object Detection and Image Segmentation Techniques

Machine vision-based automatic lamb identification and drinking activity in a commercial farm.

Using ear tags, farmers can track specific data for individual lambs such as age, medical records, body condition scores, genetic abnormalities; to make data-based decisions. However, automatic reading of ear tags using Radio Frequency Identification requires (a) an antenna, (b) a reader, (c) comparable reading standards; consequently, such a system can be expensive and impractical for a large group of lambs, especially in situations where animals are not required to have a compulsory Electronic identification, contrary to the case in Europe, where it is mandatory. Therefore, this paper proposes a machine vision system for indoor animals to identify individual lambs using existing ear tags. Using a camera that is installed such that the trough is visible, the drinking behaviour of the lambs can be automatically monitored. Data from different lamb groups in two different pens were collected. The identification algorithm includes a number of steps: (1) Detecting the lambs' face, and its ear tags in each image; (2) Cropping each ear tag image and discerning the digits on it to obtain the tag number; (3) Tracking each lamb throughout the visit using a tracking algorithm; (4) Recovering the ear tag number using an algorithm that incorporates a list of the ear tag numbers of the lambs in each pen, and the predictions for each lamb in each frame. The You Only Look Once deep learning object detection algorithm was applied to locate and localise the lamb's face and the digits in an image. The models' datasets contained 1160 and 2165 images for the training set, and 325 and 616 images for the validation set, respectively. The algorithm output includes the identity of each lamb that came to drink, and its duration. The identification system resulted in a total accuracy of 93% for the data tested, which consisted of approximately 900 visits to the drinking stations, and was collected in real time in a natural environment. The ground truth of each video of a visit was obtained by human observation by studying the video. We checked if there was indeed a visit to the water trough and if so we registered the ear tag number of each lamb whose head was above the water trough. Thus, identifying lambs in a commercial pen using a relatively inexpensive and easily installed system consisting of a RGB camera and a computer vision-based algorithm has potential for farm management.

Detection of Power Poles in Orchards Based on Improved Yolov5s Model

During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to quickly and accurately detect wire poles, and proposed an improved Yolov5s deep learning object detection algorithm named Yolov5s-Pole. The algorithm enhances the model’s generalization ability and robustness by applying Mixup data augmentation technique, replaces the C3 module with the GhostBottleneck module to reduce the model’s parameters and computational complexity, and incorporates the Shuffle Attention (SA) module to improve its focus on small targets. The results show that when the improved Yolov5s-Pole model was used for detecting poles in orchards, its accuracy, recall, and mAP@50 were 0.803, 0.831, and 0.838 respectively, which increased by 0.5%, 10%, and 9.2% compared to the original Yolov5s model. Additionally, the weights, parameters, and GFLOPs of the Yolov5s-Pole model were 7.86 MB, 3,974,310, and 9, respectively. Compared to the original Yolov5s model, these represent compression rates of 42.2%, 43.4%, and 43.3%, respectively. The detection time for a single image using this model was 4.2 ms, and good robustness under different lighting conditions (dark, normal, and bright) was demonstrated. The model is suitable for deployment on agricultural UAVs’ onboard equipment, and is of great practical significance for ensuring the efficiency and flight safety of agricultural UAVs.

Design and Implementation for BIC Code Recognition System of Containers using OCR and CRAFT in Smart Logistics

The BIC (Bureau International des Containers et du Transport Intermodal) Code is the identification code for ocean shipping containers and is crucial for logistics, transportation, and security. Accurate recognition of container BIC Code is essential for efficient import and export processes, authorities' ability to intercept illegal goods and safe transportation. Nevertheless, the current practice of employees recognizing and manually entering container BIC codes is inefficient and prone to error. Although automated recognition efforts have been made, challenges remain due to the aging of containers, manufacturing differences between companies, and the mixing of letters and numbers in the 11-digit combination. In this paper, we propose the design and implementation of a BIC Code recognition system using an open source-based OCR engine, deep learning object detection algorithm, and text detector model. In the logistics industry, various attempts are being made to seamlessly link the data required at each stage of transportation between these systems. If we can secure the stability and consistency of BIC Code recognition that can be used in the field through our research, it will contribute to overcoming the instability caused by false positives.

WT-YOLOM: An Improved Target Detection Model Based on YOLOv4 for Endogenous Impurity in Walnuts

Since impurities produced during walnut processing can cause serious harm to human health, strict quality control must be carried out during production. However, most detection equipment still uses photoelectric detection technology to automatically sort heterochromatic particles, which is unsuitable for detecting endogenous foreign bodies with similar colors. Therefore, this paper proposes an improved YOLOv4 deep learning object detection algorithm, WT-YOLOM, for detecting endogenous impurities in walnuts—namely, oily kernels, black spot kernels, withered kernels, and ground nutshells. In the backbone of the model, a lightweight MobileNet module was used as the encoder for the extraction of features. The spatial pyramid pooling (SPP) structure was improved to spatial pyramid pooling—fast (SPPF), and the model size was further reduced. Loss function was replaced in this model with a more comprehensive SIoU loss. In addition, efficient channel attention (ECA) mechanisms were applied after the backbone feature map to improve the model’s recognition accuracy. This paper compares the recognition speed and accuracy of the WT-YOLOM algorithm with the Faster R-CNN, EfficientDet, CenterNet, and YOLOv4 algorithms. The results showed that the average precision of this model for different kinds of endogenous impurities in walnuts reached 94.4%. Compared with the original model, the size was reduced by 88.6%, and the recognition speed reached 60.1 FPS, which was an increase of 29.0%. The metrics of the WT-YOLOM model were significantly better than those of comparative models and can significantly improve the detection efficiency of endogenous foreign bodies in walnuts.

U-YOLOv7: A network for underwater organism detection

Detecting and monitoring underwater organisms is very important for sea aquaculture. The human eye struggles to quickly distinguish between aquatic species due to their variety and dense dispersion. In this paper, a deep learning object detection algorithm based on YOLOv7 is used to design a new network, called Underwater-YOLOv7 (U-YOLOv7), for underwater organism detection. This model satisfies the requirements with regards to both speed and accuracy. First, a network combining CrossConv and an efficient squeeze-excitation module is created. This network increases the extraction of channel information while reducing parameters and enhancing the feature fusion of the network. Second, a lightweight Content-Aware ReAssembly of FEatures (CARAFE) operator is used to obtain more semantic information about underwater images before feature fusion. A 3D attention mechanism is incorporated to improve the anti-interference ability of the model in underwater recognition. Finally, a decoupling head using hybrid convolution is designed to accelerate convergence and improve the accuracy of underwater detection. The results show that the network proposed in this paper obtains an improvement of 3.2% in accuracy, 2.3% in recall, and 2.8% in the mean average precision value and runs at up to 179 fps, far outperforming other advanced networks. Moreover, it has a higher estimation accuracy than the YOLOv7 network.