Object Detection Technology Research Articles

A Review of Deep Learning Based Object Detection Algorithms

With the rapid development of artificial intelligence technology, the traditional manual feature-based object detection method has been gradually replaced by deep learning-based object detection technology. Object detection is the basis and prerequisite for many computer vision tasks aimed at recognizing targets in an image and determining their class and location. The purpose of object detection algorithm research based on deep learning is to improve the detection accuracy and detection speed of the detection algorithm, so that the object detection algorithm can be more safe and convenient applied to People's Daily production and life. This article reviews the evolution of object detection algorithms, focusing on two-stage, one-stage and object detection algorithms based on the transformer architecture. In addition, the performance, advantages and disadvantages of different algorithms are compared, and the development trend of object detection algorithms based on deep learning is summarized in the end.

Smart traffic monitoring with YOLOv9 object detection algorithm

Rapid advancements in artificial intelligence technology have enabled computer vision to be utilized across a wide range of engineering disciplines. This study examines the practical solutions offered by image processing technology in manual counting applications and the accuracy of advanced algorithms. The applicability and performance of the YOLOv9 algorithm in traffic counts have been evaluated. The research shows that the algorithm operates with high accuracy and minimizes human error. The study involves classification and counting operations for three different types of vehicles. According to the results, cars and trucks are detected with over 95% accuracy, while smaller objects like motorcycles have slightly lower accuracy. The effective use of YOLOv9 in vehicle counting and traffic management applications highlights the importance of object detection technology in intelligent transportation systems. This study demonstrates the potential of this technology to improve efficiency in traffic management, providing guidance for future applications. The key role that advanced algorithms like YOLOv9 can play in the development of intelligent transportation systems is an important topic for future researchers and industry professionals.

Multi-feature enhancement based on sparse networks for single-stage 3D object detection

In the field of autonomous driving, the accuracy and real-time requirements for 3D object detection technology continue to improve, which is directly related to the commercialization process and market popularity of autonomous vehicles. Despite the efficiency of pillar-based coding for onboard systems, it falls short in terms of accuracy and the reduction of incorrect positives. In this paper, we will examine how to solve the problem of high incorrect rate and low accuracy of existing methods. Firstly, a MAP coding module is introduced to optimize previous point cloud feature coding modules, allowing for the efficient extraction of fine-grained features from point cloud data. Then, we introduce an innovative sparse dual attention (SDA) to efficiently filter out irrelevant details in feature extraction, thereby improving the pertinence and efficiency of information extraction. Finally, to address the potential loss of information from single local feature extraction, a local and global fusion module (CTGC) is introduced. Our method has proactively demonstrated its efficiency and accuracy through rigorous experimentation across diverse datasets. Analysis of the results leads to the conclusion that our solutions provide accurate and robust detection results. Code will be available at https://github.com/lcy199905/MyOpenPCDet.git.

Enhancing Surveillance Systems with Cost-Effective Ultrasonic Radar for Object Detection and Tracking

Object detection is critical in surveillance systems because it enables timely identification of unauthorized objects, which is essential for maintaining security and mitigating risks. Existing approaches to surveillance typically suffer from limited coverage, variable detection range, moderate accuracy, high cost, and complex setup requirements. This article presents an approach to object detection using microcontroller-based ultrasonic sensors. Our approach leverages four distinct processing phases to create a radar-like system capable of real-time detection and autonomous decision-making. In this study, we conducted experiments by integrating more than six peripheral modules with the Arduino platform. By evaluating 17 diverse test cases in various scenarios and environments, the approach demonstrated enhanced object tracking and robustness compared to existing methods. The system effectively detects objects within the targeted area, providing precise distance measurements (around 50 cm) and position information (0 to 360 degrees). Moreover, our approach enabled the identification of three risk zones—medium, high-risk, and mild danger—within the critical region, contributing to improved security and risk management. The results reveal the system’s effectiveness in real-time detection, accurate distance estimation, and comprehensive risk assessment. Overall, our study significantly contributes to the advancement of object detection technology by effectively addressing the limitations of existing methods and offering a cost-effective and practical solution. Our research has the potential to monitor objects in surveillance systems across various domains, with significant implications for enhancing security, risk mitigation, and monitoring in industries reliant on effective object detection systems. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 157−171

Mitigating Adversarial Attacks in Object Detection through Conditional Diffusion Models

The field of object detection has witnessed significant advancements in recent years, thanks to the remarkable progress in artificial intelligence and deep learning. These breakthroughs have significantly enhanced the accuracy and efficiency of detecting and categorizing objects in digital images. Nonetheless, contemporary object detection technologies have certain limitations, such as their inability to counter white-box attacks, insufficient denoising, suboptimal reconstruction, and gradient confusion. To overcome these hurdles, this study proposes an innovative approach that uses conditional diffusion models to perturb adversarial examples. The process begins with the application of a random chessboard mask to the adversarial example, followed by the addition of a slight noise to fill the masked area during the forward process. The adversarial image is then restored to its original form through a reverse generative process that only considers the masked pixels, not the entire image. Next, we use the complement of the initial mask as the mask for the second stage to reconstruct the image once more. This two-stage masking process allows for the complete removal of global disturbances and aids in image reconstruction. In particular, we employ a conditional diffusion model based on a class-conditional U-Net architecture, with the source image further conditioned through concatenation. Our method outperforms the recently introduced HARP method by 5% and 6.5% in mAP on the COCO2017 and PASCAL VOC datasets, respectively, under non-APT PGD attacks. Comprehensive experimental results confirm that our method can effectively restore adversarial examples, demonstrating its practical utility.

Chinese Bayberry Detection in an Orchard Environment Based on an Improved YOLOv7-Tiny Model

The precise detection of Chinese bayberry locations using object detection technology is a crucial step to achieve unmanned harvesting of these berries. Because of the small size and easy occlusion of bayberry fruit, the existing detection algorithms have low recognition accuracy for such objects. In order to realize the fast and accurate recognition of bayberry in fruit trees, and then guide the robotic arm to carry out accurate fruit harvesting, this paper proposes a detection algorithm based on an improved YOLOv7-tiny model. The model introduces partial convolution (PConv), a SimAM attention mechanism and SIoU into YOLOv7-tiny, which enables the model to improve the feature extraction capability of the target without adding extra parameters. Experimental results on a self-built Chinese bayberry dataset demonstrate that the improved algorithm achieved a recall rate of 97.6% and a model size of only 9.0 MB. Meanwhile, the precision of the improved model is 88.1%, which is 26%, 2.7%, 4.7%, 6.5%, and 4.7% higher than that of Faster R-CNN, YOLOv3-tiny, YOLOv5-m, YOLOv6-n, and YOLOv7-tiny, respectively. In addition, the proposed model was tested under natural conditions with the five models mentioned above, and the results showed that the proposed model can more effectively reduce the rates of misdetections and omissions in bayberry recognition. Finally, the improved algorithm was deployed on a mobile harvesting robot for field harvesting experiments, and the practicability of the algorithm was further verified.

GLIC: Underwater target detection based on global–local information coupling and multi-scale feature fusion

With the rapid development of object detection technology, underwater object detection has attracted widespread attention. Most of the existing underwater target detection methods are built based on convolutional neural networks (CNNs), which still have some limitations in the utilization of global information and cannot fully capture the key information in the images. To overcome the challenge of insufficient global–local feature extraction, an underwater target detector (namely GLIC) based on global–local information coupling and multi-scale feature fusion is proposed in this paper. Our GLIC consists of three main components: spatial pyramid pooling, global–local information coupling, and multi-scale feature fusion. Firstly, we embed spatial pyramid pooling, which improves the robustness of the model while retaining more spatial information. Secondly, we design the feature pyramid network with global–local information coupling. The global context of the transformer branch and the local features of the CNN branch interact with each other to enhance the feature representation. Finally, we construct a Multi-scale Feature Fusion (MFF) module that utilizes balanced semantic features integrated at the same depth for multi-scale feature fusion. In this way, each resolution in the pyramid receives equal information from others, thus balancing the information flow and making the features more discriminative. As demonstrated in comprehensive experiments, our GLIC, respectively, achieves 88.46%, 87.51%, and 74.94% mAP on the URPC2019, URPC2020, and UDD datasets.

Weakly Supervised Underwater Object Real-time Detection Based on High-resolution Attention Class Activation Mapping and Category Hierarchy

Recently, deep learning-based underwater object detection technology has achieved remarkable success. However, the accuracy and completeness of dataset instance annotation are crucial for its success. The quality of underwater images is low, severe objects clustering, and occlusion, acquiring object's annotations demands substantial time and labor costs, while mis annotation and missed annotation can also degrade model performance and limit their application in practical scenarios. To address this issue, this paper presents a novel weakly supervised underwater object real-time detection method, which is divided into two subtasks: weakly supervised object localization and real-time object detection. In the weakly supervised object localization task, we design a novel category hierarchy structure network that integrates the high-resolution attention-class activation mapping algorithm to obtain high-quality object class activation maps, weaken background interference, and obtain more complete object regions. The parameterized spatial loss module is devised to enable the model to escape from local optimal solutions, thus accurately and efficiently obtaining object pseudo-detection annotation boxes. For the real-time object detection task, the single-stage detector YOLOv7 is selected as the basic detection model, and an object perception loss function is designed based on the class activation map to jointly supervise the training process. A method for filtering noisy pseudo-supervision information is proposed to enhance the pseudo-supervision information involved in training. Ablation experiments and multi-method comparison experiments were conducted on the URPC and RUOD datasets, and the results verify the effectiveness of the proposed strategy, and our model exhibits significant advantages in detection performance and detection efficiency compared to current mainstream and advanced models.

Degradation Type-Aware Image Restoration for Effective Object Detection in Adverse Weather

Despite significant advancements in CNN-based object detection technology, adverse weather conditions can disrupt imaging sensors’ ability to capture clear images, thereby adversely impacting detection accuracy. Mainstream algorithms for adverse weather object detection enhance detection performance through image restoration methods. Nevertheless, the majority of these approaches are designed for a specific degradation scenario, making it difficult to adapt to diverse weather conditions. To cope with this issue, we put forward a degradation type-aware restoration-assisted object detection network, dubbed DTRDNet. It contains an object detection network with a shared feature encoder (SFE) and object detection decoder, a degradation discrimination image restoration decoder (DDIR), and a degradation category predictor (DCP). In the training phase, we jointly optimize the whole framework on a mixed weather dataset, including degraded images and clean images. Specifically, the degradation type information is incorporated in our DDIR to avoid the interaction between clean images and the restoration module. Furthermore, the DCP makes the SFE possess degradation category awareness ability, enhancing the detector’s adaptability to diverse weather conditions and enabling it to furnish requisite environmental information as required. Both the DCP and the DDIR can be removed according to requirement in the inference stage to retain the real-time performance of the detection algorithm. Extensive experiments on clear, hazy, rainy, and snowy images demonstrate that our DTRDNet outperforms advanced object detection algorithms, achieving an average mAP of 79.38% across the four weather test sets.

Evaluating efficiency of foreign object detection technology based on the use of passive infrared thermography

A new method for detecting subsurface solid objects buried in farmlands, such as plastic bottles, wasted cans, etc., has been proposed by applying the technique of infrared (IR) thermography to monitor the temperature of soil surface subjected to solar irradiation. Through both experimentation and simulation, this study parameterizes the influence of environmental factors on IR images and validates the detection capabilities of the method. To verify the feasibility of IR thermography testing, the experimental section of the work is devoted to monitoring aluminum and polyethylene terephthalate cans buried in sand with varying grades of moisture. The dependencies between the efficiency of foreign object detection and their depth are derived. A restoring pseudothermal flux algorithm was used to reduce the impact of lateral diffusion on IR thermographic detection of foreign objects buried in soil. Variations of soil temperature caused by varying solar radiation during multiple day-night cycles are used to improve the detectable diameter-depth ratio. The described technique is efficient and provides no harm to human beings.

CP-RDM: a new object detection algorithm for casting and pouring robots

Abstract Automating the casting sector heavily relies on pivotal technology for object detection in pouring robots. A sophisticated algorithm designed to identify and locate target pouring holes in intricate casting workshops is crucial for advancing the intelligence of the casting process. However, the workshop environment for pouring is generally challenging, with uneven lighting, varying sizes of pouring holes, and significant occlusion in the target area, all impacting the accuracy of target detection tasks.
To overcome these challenges, this paper proposes enhancing the YOLOv8s algorithm for object detection in pouring robots. Firstly, to address the issue of different scales in pouring holes, a Multi-Scale Residual Channel and Spatial Information Fusion Module (MRCS) is designed to aggregate channel and spatial information, thereby enhancing the feature extraction capability of the model. The proposed enhancement is validated on the Pascal VOC dataset. Secondly, a SimAM attention mechanism is added at the end of the backbone network to focus the object detection network more on the positional region of the pouring hole. Importantly, this addition does not introduce extra parameters or computational burden to the model.
Finally, in the detection part of the model, the detection head from the RT-DETR model is introduced. This combination of real-time detection capability from YOLO and deep feature extraction capability from RT-DETR enhances the detection accuracy of the model while ensuring real-time performance. Experimental results on the updated pouring hole dataset reveal that, with only a slight increase in parameters, the proposed model achieves a 2.5% and 3.5% improvement in mAP@0.5 and F1-Score, respectively, compared to the baseline algorithm YOLOv8s. Precision (P) is enhanced by 1.8%, recall (R) by 3.5%, and PFS reaches 110, meeting the requirements for real-time pouring in pouring robots.

Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review.

Non-destructive testing (NDT) techniques play a crucial role in industrial production, aerospace, healthcare, and the inspection of special equipment, serving as an indispensable part of assessing the safety condition of pressure equipment. Among these, the analysis of NDT data stands as a critical link in evaluating equipment safety. In recent years, object detection techniques have gradually been applied to the analysis of NDT data in pressure equipment inspection, yielding significant results. This paper comprehensively reviews the current applications and development trends of object detection algorithms in NDT technology for pressure-bearing equipment, focusing on algorithm selection, data augmentation, and intelligent defect recognition based on object detection algorithms. Additionally, it explores open research challenges of integrating GAN-based data augmentation and unsupervised learning to further enhance the intelligent application and performance of object detection technology in NDT for pressure-bearing equipment while discussing techniques and methods to improve the interpretability of deep learning models. Finally, by summarizing current research and offering insights for future directions, this paper aims to provide researchers and engineers with a comprehensive perspective to advance the application and development of object detection technology in NDT for pressure-bearing equipment.

Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods.

AID-YOLO: An Efficient and Lightweight Network Method for Small Target Detector in Aerial Images

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the mAP@0.5 metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the mAP@0.5 metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection.

Improved Architecture and Training Strategies of YOLOv7 for Remote Sensing Image Object Detection

The technology for object detection in remote sensing images finds extensive applications in production and people’s lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, after analyzing the requirements and difficulties in the detection of remote sensing images. Specifically, we strategically remove some standard convolution and pooling modules from the bottom of the network, adopting stride-free convolution to minimize the loss of information for small objects in the transmission. Simultaneously, we introduce a new, more efficient attention mechanism module for feature extraction, significantly enhancing the network’s semantic extraction capabilities. Furthermore, by adding multiple cross-layer connections in the network, we more effectively utilize the feature information of each layer in the backbone network, thereby enhancing the network’s overall feature extraction capability. During the training phase, we introduce an auxiliary network to intensify the training of the underlying network and adopt a new activation function and a more efficient loss function to ensure more effective gradient feedback, thereby elevating the network performance. In the experimental results, our improved network achieves impressive mAP scores of 91.2% and 80.8% on the DIOR and DOTA version 1.0 remote sensing datasets, respectively. These represent notable improvements of 4.5% and 7.0% over the original YOLOv7 network, significantly enhancing the efficiency of detecting small objects in particular.

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

A review of the development of YOLO object detection algorithm

The You Only Look Once (YOLO) algorithm series, as the forefront of object detection technology, has evolved from YOLOv1 to YOLOv10, consistently enhancing detection speed and accuracy. Through literature review and data analysis. This paper mainly discusses the development processes of the YOLO algorithm series, focuses on the changes and innovations in network structure, training strategies, and performance optimization. By introducing techniques, such as CSPNet, Anchor-free, data augmentation, and multi-scale training, the YOLO algorithm has progressively found a better balance between detection speed and accuracy, demonstrating excellent performance in processing real-time images and handling high-complexity scenarios. Furthermore, this paper also addresses some challenges faced by the YOLO algorithms and potential future research directions, such as the lower accuracy in detecting small targets and reduced robustness in complex scenarios. Through analysis, potential optimization directions for the future include further refining network structure and employing more efficient training methods to enhance algorithm efficiency. This paper intends to offer a thorough performance evaluation of the YOLO series algorithms, identify potential areas for future improvements to advance YOLO technology.

Visual object detection system based on augmented reality and steady-state visual evoked potential

This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.

A novel combined method for conveyor belt deviation discrimination under complex operational scenarios

Conveyor belt can enhance transportation efficiency and reduce labor intensity, which is extensively applied in material transportation. However, the deviation of conveyor belt commonly leads to malfunctions in the transmission system, affecting routine transportation function. Therefore, it is crucial to accurately identify whether the conveyor belt deviates and rectify anomalies promptly. Previously, the on-site camera have to be arranged in the appropriate position to ensure conducive recognition results for deviation discrimination, which is probably constrained by environmental conditions during implementation. In this paper, a combined method of detection and segmentation with deblurring function (CDSwD) is presented for conveyor belt deviation discrimination, which can avoid the impact of the camera position on the deviation discrimination results by combining roller information and conveyor belt contour information. Initially, the deblurring approach is adopted to remove the motion blur from the images. Subsequently, the object detection technology and the semantic segmentation technique are utilized to generate the detection results along the lower line of the rollers and the segmentation results of the overall conveyor belt contour. Finally, the outcomes will be combined for conveyor belt deviation discrimination. Comparisons have demonstrated that the proposed method can accurately determine the deviation of the conveyor belt, which is beneficial for maintaining the normal transportation and improving work efficiency.

DBSCAN and Yolov5 based 3D object detection and its adaptation to a mobile platform

This study presents a 3D object detection technology for mobile platforms and its application. Rather than an innovative high-performance model, we proposed a “useable” model for the robot industry at the current technology stage by combining various techniques. To reduce computation time, a 2D region proposal was obtained using a RGB image-based CNN model. By applying the DBSCAN clustering technique to the point cloud corresponding to the 2D region proposal, a method of obtaining a 3D region proposal was proposed. This allowed for 3D object detection using an RGB image dataset, which has been widely researched, while reducing the computation load to a level suitable for use in mobile robots. Furthermore, the 3D object detection was integrated into a ROS 2-based mobile platform, which was used to perform pedestrian-safe avoidance tasks and elevator button operation tasks. The performance was confirmed through experiments.