Deep Learning Object Detection Research Articles

MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision

Millimeter-wave radar and machine vision are both important means for intelligent vehicles to perceive the surrounding environment. Aiming at the problem of multi-sensor fusion, this paper proposes the object detection method of millimeter-wave radar and vision fusion. Radar and camera complement each other, and radar data fusion in machine vision network can effectively reduce the rate of missed detection under insufficient light conditions, and improve the accuracy of remote small object detection. The radar information is processed by mapping transformation neural network to obtain the mask map, so that radar information and visual information in the same scale. A multi-data source deep learning object detection network (MS-YOLO) based on millimeter-wave radar and vision fusion was proposed. Homemade datasets were used for training and testing. This maximized the use of sensor information and improved the detection accuracy under the premise of ensuring the detection speed. Compared with the original YOLOv5 (the fifth version of the You Only Look Once) network, the results show that the MS-YOLO network meets the accuracy requirements better. Among the models, the large model of MS-YOLO has the highest accuracy with an mAP reaching 0.888. The small model of MS-YOLO has good accuracy and speed, and the mAP reaches 0.841 while maintaining a high frame rate of 65 fps.

Comparison between convolutional neural network and K-nearest neighbours object detection for autonomous drone

In autonomous drones, the drone’s ability to move depends on the drone’s capacity to know its position, either in relative or absolute position. The Pinhole model is one of the methods to calculate a drone’s relative position based on the triangle similarity concept using a single camera. This method utilizes bounding box information generated from an object detection algorithm. Thus, accuracy of the generated bounding box is crucial, and selection of object detection algorithm is necessary. This paper compares and evaluates machine learning and deep learning object detection methods to determine which method is suitable for distance measurement using a single camera for autonomous drone’s controller based on pinhole model. A novel K-nearest neighbours-based (KNN-based) object detection is constructed to represent the machine learning method while you only look once version 5 (YOLOv5) convolutional neural network (CNN) architecture is selected to represent the deep learning method. A dataset consists of two different classes, with a total of 1520 images, collected from the unmanned aerial vehicle (UAV) camera for training and evaluation purposes. Confusion matrix and intersection over union (IoU)/generalized intersection of union (GIoU) matrix are used to evaluate the performance of both methods. The result of this paper shows the performance of each system and concludes the suitable type of object detection algorithm for the autonomous UAV purpose.

Vision-based method for semantic information extraction in construction by integrating deep learning object detection and image captioning

Recently, vision-based monitoring has been widely adopted in construction management to improve crew productivity, reduce safety risks, and facilitate site planning. However, automated retrieval of semantic information (e.g., objects, activities, and interactions between objects) from construction images remains challenging due to the complex nature of construction sites. This paper proposes a novel semantic information extraction method by integrating deep learning object detection and image captioning, which aims to explore salient information from construction images or videos. In the proposed method, object detection has been employed as an encoder to extract the feature maps of construction object zones and the holistic image. The image captioning has been selected as the decoder to extract the semantic information. A post-processing method has been proposed to parse the semantic information into a graph format for better accessibility and visualization. In experiments, the proposed method has achieved the Consensus-based Image Description Evaluation (CIDEr) of 1.84. By adopting the proposed method, semantic information behind construction images can be presented to construction managers to assist their decision-making.

Using Clean Energy Satellites to Interpret Imagery: A Satellite IoT Oriented Lightweight Object Detection Framework for SAR Ship Detection

This paper studies the lightweight deep learning object detection algorithm to detect ship targets in SAR images that can be deployed on-orbit and accessed in the space-based IoT. Traditionally, remote sensing data must be transferred to the ground for processing. With the vigorous development of the commercial aerospace industry, computing, and high-speed laser inter-satellite link technologies, the interconnection of everything in the intelligent world has become an irreversible trend. Satellite remote sensing has entered the era of a big data link with IoT. On-orbit interpretation gives remote sensing images expanse application space. However, implementing on-orbit high-performance computing (HPC) is difficult; it is limited by the power and computer resource consumption of the satellite platform. Facing this challenge, building a processing algorithm with less computational complexity, less parameter quantity, high precision, and low computational power consumption is a key issue. In this paper, we propose a lightweight end-to-end SAR ship detector fused with the vision transformer encoder: YOLO−ViTSS. The experiment shows that YOLO−ViTSS has lightweight features, the model size is only 1.31 MB; it has anti-noise capability is suitable for processing SAR remote sensing images with native noise, and it also has high performance and low training energy consumption with 96.6 mAP on the SSDD dataset. These characteristics make YOLO−ViTSS suitable for porting to satellites for on-orbit processing and online learning. Furthermore, the ideas proposed in this paper help to build a cleaner and a more efficient new paradigm for remote sensing image interpretation. Migrating HPC tasks performed on the ground to on-orbit satellites and using solar energy to complete computing tasks is a more environmentally friendly option. This environmental advantage will gradually increase with the current construction of large-scale satellite constellations. The scheme proposed in this paper helps to build a novel real-time, eco-friendly, and sustainable SAR image interpretation mode.

Cloud-Based Architecture for YOLOv3 Object Detector using gRPC and Protobuf

The deep learning-based object detector accuracy has surpassed conventional detection methods. Although implementation is still limited to hardware capabilities, this problem can be overcome by combining edge devices with cloud computing. The recent study of cloud-based object detector architecture is generally based on representational state transfer (RESTful web services), which uses a pooling system method for data exchange. As a result, this system leads to a low detection speed and cannot support real-time data streaming. Therefore, this study aims to enhance the detection speed in cloud-based object recognition systems using gRPC and Protobuf to support real-time detection. The proposed architecture was deployed on the Virtual Machine Instance (VMI) equipped with a Graphics Processing Unit (GPU). The gRPC server and YOLOv3 deep learning object detector were executed on the cloud server to handle detection requests from edge devices. Furthermore, the captured images from the edge devices were encoded into Protobuf format to reduce the message size delivered to the cloud server. The results showed that the proposed architecture improved detection speed performance on the client-side in the range of 0.27 FPS to 1.72 FPS compared to the state-of-the-art method. It was also observed that it could support multiple edge devices connection with slight performance degradation in the range of 1.78 FPS to 1.83 FPS, depending on the network interface used.

Research on the Application of Object Detection in the Installation of Spacer Bars

The correct installation of spacer bars is an important part of ensuring people’s daily life. This paper studies and implements a spacer bar installation confirmation system based on a deep learning object detection algorithm, which makes up for the deficiencies in the installation. Among the object detection algorithms, Faster-RCNN has higher detection accuracy, is more accurate than one-stage and can detect multi-scale and small objects. Through historical use, it is found that Faster-RCNN is excellent in detecting multi-class image sets tasks. For personal data sets, only fine-tuning is needed to achieve better results, and it also has a fast detection rate. Therefore, this paper is based on the Faster-RCNN algorithm to realize the detection and confirmation of the plug and rubber during the installation of the spacer bar.

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can diagnose brain tumors by employing magnetic resonance imaging (MRI) as a supporting technology. We propose transfer learning approaches for a deep learning model to detect malignant tumors, such as glioblastoma, using MRI scans in this study. This paper presents a deep learning-based approach for brain tumor identification and classification using the state-of-the-art object detection framework YOLO (You Only Look Once). The YOLOv5 is a novel object detection deep learning technique that requires limited computational architecture than its competing models. The study used the Brats 2021 dataset from the RSNA-MICCAI brain tumor radio genomic classification. The dataset has images annotated from RSNA-MICCAI brain tumor radio genomic competition dataset using the make sense an AI online tool for labeling dataset. The preprocessed data is then divided into testing and training for the model. The YOLOv5 model provides a precision of 88 percent. Finally, our model is tested across the whole dataset, and it is concluded that it is able to detect brain tumors successfully.

A Survey on Human Cancer Categorization Based on Deep Learning.

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

Detection and classification of whiteflies and development stages on soybean leaves images using an improved deep learning strategy

This paper presents a novel strategy to detect and classify adult whiteflies and five important related stages on images of detached soybean leaves. The whitefly Bemisia tabaci is a major pest in soybean crops, and by detecting, counting and differentiating its related life stages in field collected leaves control management decisions can be made. The proposed solution is based on a deep learning object detection algorithm (YOLOv4), modified into an specific new learning strategy with innovations on data augmentation, image mosaicking, and fusion of hypothesized object categories. A real and annotated dataset of images is provided from a controlled experiment infected with whitefly eggs having 121 images and 973 annotated objects. The experimental results showed a promising performance of the proposed system, reaching an f1-score of 0.87, in comparison with a single YOLOv4 algorithm that reached f1-score of 0.80. The overall strategy could be extended to work in other similar tasks for image based pest management.

An automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google street view images

Tree species and canopy structural profile (‘tree profile’) are among the most critical environmental factors in determining urban ecosystem services such as climate and air quality control from urban trees. To accurately characterize a tree profile, the tree diameter, height, crown width, and height to the lowest live branch must be all measured, which is an expensive and time-consuming procedure. Recent advances in artificial intelligence aids to efficiently and accurately measure the aforementioned tree profile parameters. This can be particularly helpful if spatially extensive and accurate street-level images provided by Google (‘streetview’) or Kakao (‘roadview’) are utilized. We focused on street trees in Seoul, the capital city of South Korea, and suggested a novel approach to create a tree profile and inventory based on deep learning algorithms. We classified urban tree species using the YOLO (You Only Look Once), one of the most popular deep learning object detection algorithms, which provides an uncomplicated method of creating datasets with custom classes. We further utilized semantic segmentation algorithm and graphical analysis to estimate tree profile parameters by determining the relative location of the interface of tree and ground surface. We evaluated the performance of the model by comparing the estimated tree heights, diameters, and locations from the model with the field measurements as ground truth. The results are promising and demonstrate the potential of the method for creating urban street tree profile inventory. In terms of tree species classification, the method showed the mean average precision (mAP) of 0.564. When we used the ideal tree images, the method also reported the normalized root mean squared error (NRMSE) for the tree height, diameter at breast height (DBH), and distances from the camera to the trees as 0.24, 0.44, and 0.41.

Cycle Performance of Aerated Lightweight Concrete Windowed and Windowless Wall Panel from the Perspective of Lightweight Deep Learning.

This paper aims to explore the seismic mechanical properties of newly developed fabricated aerated lightweight concrete (ALC) wall panels to clarify the interaction mechanism between wall panels and structures. It first introduces the lightweight deep learning object detection algorithm and constructs a network model with faster operation speed based on the convolutional neural network. Secondly, combined with the deep learning object detection algorithm, the quasi-static loading system is adopted to conduct the repeated loading test on two fabricated ALC wall panels. Finally, the hysteresis load-displacement curve of each test is recorded. The experimental results show that the proposed deep learning algorithm greatly improves the operation speed and compresses the model size without reducing the accuracy. The lightweight deep learning algorithm is applied to the study of the slip performance of the wall plate. The pretightening force of the connecting screw characterizes the slip performance between the wall plate and the structural beam, thereby affecting the deformation response of the wall plate when the interstory displacement increases. The hysteresis curve of the ALC wall panel has obvious squeezing effect, indicating that the slip of the connector can unload part of the external load and delay the damage of the wall panel. The skeleton curve suggests that the fabricated windowless ALC wall panel has higher positive and negative initial stiffness and bearing capacity than the fabricated windowed wall panel. However, the degradation analysis of the stiffness curve reveals that the lateral stiffness deviation of the fabricated windowless ALC wall panel is more obvious. It confirms that the proposed connection method based on the lightweight deep learning model can improve the seismic performance of ALC wall panels and provide reference for the structural analysis of embedding fabricated ALC wall panels. This work shows the important practical value for exploring the application effect of embedded ALC wall panels.

Benchmarking Object Detection Deep Learning Models in Embedded Devices.

Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very high accuracy albeit with high computational costs. Due to the physical limitations of robotic platforms, embedded devices are not as powerful as desktop computers, and adjustments have to be made to deep learning models before transferring them to robotic applications. This work benchmarks deep learning object detection models in embedded devices. Furthermore, some hardware selection guidelines are included, together with a description of the most relevant features of the two boards selected for this benchmark. Embedded electronic devices integrate a powerful AI co-processor to accelerate DL applications. To take advantage of these co-processors, models must be converted to a specific embedded runtime format. Five quantization levels applied to a collection of DL models are considered; two of them allow the execution of models in the embedded general-purpose CPU and are used as the baseline to assess the improvements obtained when running the same models with the three remaining quantization levels in the AI co-processors. The benchmark procedure is explained in detail, and a comprehensive analysis of the collected data is presented. Finally, the feasibility and challenges of the implementation of embedded object detection applications are discussed.

Proposing Lane and Obstacle Detection Algorithm Using YOLO to Control Self-Driving Cars on Advanced Networks

Developing self-driving cars is an important foundation for the development of intelligent transportation systems with advanced telecommunications network infrastructure such as 6G networks. The paper mentions two main problems, namely, lane detection and obstacle detection (road signs, traffic lights, vehicles ahead, etc.) through image processing algorithms. To solve problems such as low detection accuracy of traditional image processing methods and poor real-time performance of methods based on deep learning methods, lane and object detection algorithm barriers for smart traffic are proposed. We first convert the distorting image caused by the camera and use a threshold algorithm for the lane detection algorithm. The image with a top-down view is then determined through the extraction of a region of interest and inverse perspective transform. Finally, we implement the sliding window method to determine pixels belonging to each lane and adapt it to a quadratic equation. YOLO algorithm is suitable for identifying many types of obstacles for identification problems. Finally, we use real-time videos and the TuSimple dataset to perform simulations for the proposed algorithm. The simulation results show that the accuracy of the proposal for detecting lanes is 97.91% and the processing time is 0.0021 seconds. The accuracy of the proposal for detecting obstacles is 81.90%, and the processing time is 0.022 seconds. Compared with the traditional image processing method, the average accuracy and execution time of the proposed method are 89.90% and 0.024 seconds, which is a strong antinoise ability. The results prove that the proposed algorithm can be deployed for self-driving car systems with a high processing speed of the advanced network.

Deep learning object detection in materials science: Current state and future directions

Deep learning-based object detection models have recently found widespread use in materials science, with rapid progress made in just the past two years. Scanning and tunneling electron microscopy methods are among the most important and widely used characterization techniques for understanding fundamental materials structure–property-performance linkages from the micron to atomic scale. Dramatic increases in dataset size and complexity from modern electron microscopy instruments have necessitated the development and use of automated methods of extracting pertinent features of images. Here, the use of object detection in materials science, with a focus on the analysis of features in electron microscopy images, is reviewed. Key findings and limitations of recent seminal studies using object detection to characterize and quantify defects in irradiated metal alloys, segment and analyze micro and nanoparticles, find individual atoms at the nanoscale, and detect and track objects from in situ video are reviewed. Opportunities and challenges presently facing the materials community are highlighted, where discussion of best practices for model assessment and applicability are presented, along with the potential of improved model training with synthetic data. This review concludes with offering more speculative, forward-looking thoughts on the potential of the broader materials community to construct a living ecosystem integrating community-consensus curated data and validated models as tools to best inform application of object detection and segmentation models to specific materials domains.

Complete and accurate holly fruits counting using YOLOX object detection

Fruits counting is important in management of orchard and plantation since better decision for labor and logistic can be made based on complete and accurate counting of fruits. Computer vision-based fruits counting has been research focus as it’s an automatic way for recognition of dense fruit on the branch. However, complete fruits counting of a whole tree hasn’t hardly been studied. And there is a lack of robust and accurate fruits counting method in complex orchard scenarios, like covering, shadow, clustering in image. In this paper, a panoramic method for fruit complete yield counting based on deep learning object detection is proposed, and was validated on a holly tree with dense fruits. Firstly, images were taken surrounding the fruit trees using UAV, and SIFT based images matching were performed to form a complete panoramic unfolding map of the fruit tree surface. Then, a YOLOX object detection network was built and trained with novel samples augmentation and composition strategies. Finally, fruits counting YOLOX was performed on the panorama to count the whole plant fruits number. The accuracy and effectiveness of this method were tested at different scales and scenarios. The results show that: (1) high-quality panoramic images can be built for an accurate fruit number counting. (2) The Statistical Rate (SR) between detected number and actual number is as high as SR > 96% when the ring shot parameter of Holly tree is R ≤ 1.2 m, SR > 95% when R ≤ 1.6 m. The Detection Rate between detected number and captured number in the panorama image is over 99% when R ≤ 1.2 m and over 97% when R ≤ 2.0 m. The result is superior to previous researches. (3) it has good robustness against shading, covering, incomplete contour. Comparisons between the proposed method and other methods has been done and the result show the proposed method is the most effective in fruits counting. Moreover, we proposed and verified the positive effects of Gaussian convolution kernel and γ-component control on fruit detection rate. The YOLOX-based fruit counting method can be extended to a wide range of fruits, like apples, lychee and so. Moreover, YOLOX has excellent inferencing efficiency which makes it a good potential for real-time application in orchard and plantation management.

X‐ray microscopy and automatic detection of defects in through silicon vias in three‐dimensional integrated circuits

AbstractThrough silicon vias (TSVs) are a key enabling technology for interconnection and realization of complex three‐dimensional integrated circuit (3D‐IC) components. In order to perform failure analysis without the need of destructive sample preparation, x‐ray microscopy (XRM) is a rising method of analyzing the internal structure of samples. However, there is still a lack of evaluated scan recipes or best practices regarding XRM parameter settings for the study of TSVs in the current state of literature. There is also an increased interest in automated machine learning and deep learning approaches for qualitative and quantitative inspection processes in recent years. Especially deep learning based object detection is a well‐known methodology for fast detection and classification capable of working with large volumetric XRM datasets. Therefore, a combined XRM and deep learning object detection workflow for automatic micrometer accurate defect location on liner‐TSVs was developed throughout this work. Two measurement setups including detailed information about the used parameters for either full IC device scan or detailed TSV scan were introduced. Both are able to depict delamination defects and finer structures in TSVs with either a low or high resolution. The combination of a 0.4 objective with a beam voltage of 40 kV proved to be a good combination for achieving optimal imaging contrast for the full‐device scan. However, detailed TSV scans have demonstrated that the use of a 20 objective along with a beam voltage of 140 kV significantly improves image quality. A database with 30,000 objects was created for automated data analysis, so that a well‐established object recognition method for automated defect analysis could be integrated into the process analysis. This RetinaNet‐based object detection method achieves a very strong average precision of 0.94. It supports the detection of erroneous TSVs in both top view and side view, so that defects can be detected at different depths. Consequently, the proposed workflow can be used for failure analysis, quality control or process optimization in R&D environments.

Key target and defect detection of high-voltage power transmission lines with deep learning

Automatic visual detection of key targets and defects for power transmission lines based on power transmission line inspection robots (PTLIR) and unmanned aerial vehicles (UAVs) is an ongoing trend in the smart grid development. The advancement of deep learning has accelerated the intelligence of power grid inspection. In terms of the power transmission line fittings detection application, improving the detection accuracy of small targets and defects using a deep learning detection network is challenging, owing to the complex background lighting characteristics of high-voltage power transmission lines in the wild. To address this problem, we present an inspection method for key targets and defects in high-voltage power transmission lines based on a deep learning object detection network. First, we collected sample images of key targets under different backgrounds, lighting conditions, and postures. Further, data augmentation was performed to solve the problem of imbalance in the number of target categories, and a large standard dataset was constructed. Second, we improved the extraction ability of small object features by optimizing the detection network. The precision and recall rate of the optimized detection network were 93.5% and 96.2%, respectively. Furthermore, small targets and defects in a complex environment could be successfully detected. Additionally, the detection of targets and defects in the inspection videos recorded by the PTLIR and UAVs were realized. Experimental results demonstrated that the proposed method performed well in the detection accuracy of key targets and defects in similar high-voltage power transmission line environments. It can realize remote, automatic inspection of high-voltage power transmission lines in the field.

Leveraging AI Transfer Learning for Rapid and Accurate Identification and Quantification of Cellular Biomarkers in Microscopy Images

Many fields of biology research and biomedical diagnostics utilize microscopy imaging to evaluate a broad range of cell types, subcellular structures, and pathology. The data from this imaging is crucial for evaluating experimental outcomes and inferring relationships between relevant biomarkers. With progress in imaging technology, researchers can now view much greater detail and gather larger datasets. Unfortunately, the ability to identify and quantify these biomarkers remains a bottleneck, often requiring significant hand annotation that is both time consuming and potentially introduces intra and inter‐rater error. While some automated solutions exist, they often still require a great deal of user involvement to correct inaccuracies. In recent years, computer vision emerged as an excellent solution to accurately detect regions of interest in microscopy images. The training of deep machine learning object detection models generally requires 1000s of user annotated images. Although this hand‐annotation is still a large upfront time investment, it produces a highly accurate model that provides much faster and more consistent analysis of frequently studied cell types and biomarkers. Regrettably, this method is less useful for researchers studying a novel cell type or biomarker with few training images available. To combat these limitations, we used a machine learning technique known as transfer learning to develop object detection capabilities for novel biomarkers using previously trained models with far fewer annotated images. We tested several different transfer learning methods with two of our existing cellular biomarker detection models, including wisteria floribunda agglutinin (WFA) and Parvalbumin (PV), to create new customized versions for different research labs. Our results indicated that several transfer learning methods worked well, with one performing consistently better than the others. We then tested this best method on a small number of androgen receptor (AR) images, a biomarker for which we did not have an existing detection model, and reached accuracy scores comparable to our original models for other biomarkers. Overall, by utilizing transfer learning we were able to reduce the number of required annotated images from several 1000s to 10s, with comparable or improved detection accuracy. Our results indicate that this computational method will greatly reduce time and resources needed to train accurate object detection models, thus broadening the reach of smart automated analysis to novel biomarkers and smaller datasets. Ultimately, this technology will allow the analysis bottleneck to catch up to the current hardware advances in the field, and rapidly advance biological research progress.

A Deep Learning-based Accurate Drug Detection, Identification and Confirmation Mechanism for Medication Dispensing Package of Inpatients

In this study, a deep learning based accurate drug detection, identification and confirmation mechanism (DLADDICM) for medication dispensing package is proposed for inpatients. In this proposed DLADDICM, a medication dispensing package with a printed QR code is photo taken and drugs in the image are detected and identified using a deep learning object detection algorithm, namely You Only Look Once (YOLO). The QR code information is deciphered and compared with the detected drugs to confirm the correctness of the medication dispensing. If there are mismatch situation(s), the computer with the proposed DLADDICM will generate different warning sound in responding to different incorrect situations. A data set with 30 drugs form the National Library of Medicine of NIH, USA is used for testing the DLADDICM using the object tracking and detection deep learning algorithm YOLOv3. Experimental results shown that the DLADDICM can detect and identify the incorrect drugs and generate the appropriate warning sound for the incorrect drug in pharma package for further human inspection. The experimental results also exhibits that by using the AI-enabled mechanism an accurate, safer, healthier with precision medication environment for the medical industries could also be achieved.

A Survey of Dental Caries Segmentation and Detection Techniques.

Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance.