Automatic Object Detection Research Articles

EmbedSeg: Embedding-based Instance Segmentation for Biomedical Microscopy Data.

Automatic detection and segmentation of biological objects in 2D and 3D image data is central for countless biomedical research questions to be answered. While many existing computational methods are used to reduce manual labeling time, there is still a huge demand for further quality improvements of automated solutions. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility to biomedical data is largely unexplored. Here we introduce EmbedSeg, an embedding-based instance segmentation method designed to segment instances of desired objects visible in 2D or 3D biomedical image data. We apply our method to four 2D and seven 3D benchmark datasets, showing that we either match or outperform existing state-of-the-art methods. While the 2D datasets and three of the 3D datasets are well known, we have created the required training data for four new 3D datasets, which we make publicly available online. Next to performance, also usability is important for a method to be useful. Hence, EmbedSeg is fully open source (https://github.com/juglab/EmbedSeg), offering (i)tutorial notebooks to train EmbedSeg models and use them to segment object instances in new data, and (ii)a napari plugin that can also be used for training and segmentation without requiring any programming experience. We believe that this renders EmbedSeg accessible to virtually everyone who requires high-quality instance segmentations in 2D or 3D biomedical image data.

A Novel Deep Learning-Based Relabeling Architecture for Space Objects Detection from Partially Annotated Astronomical Images

Space Surveillance and Tracking is a task that requires the development of systems that can accurately discriminate between natural and man-made objects that orbit around Earth. To manage the discrimination between these objects, it is required to analyze a large amount of partially annotated astronomical images collected using a network of on-ground and potentially space-based optical telescopes. Thus, the main objective of this article is to propose a novel architecture that improves the automatic annotation of astronomical images. To achieve this objective, we present a new method for automatic detection and classification of space objects (point-like and streaks) in a supervised manner, given real-world partially annotated images in the FITS (Flexible Image Transport System) format. Results are strongly dependent on the preprocessing techniques applied to the images. Therefore, different techniques were tested including our method for object filtering and bounding box extraction. Based on our relabeling pipeline, we can easily follow how the number of detected objects is gradually increasing after each iteration, achieving a mean average precision of 98%.

Energy-Efficient Illumination by Matrix Headlamps for Nighttime Automated Object Detection

The contribution at hand presents a novel approach to generate dynamic energy-optimized illumination by matrix headlamps designed for automated driving. The approach consists of a novel basic minimal illumination adapted to the current road environment with minimum brightness and a light pixel control strategy to adjust the illumination of potential objects uniformly and selectively only. The approach was developed and optimized by considering the impact of other lighting functions such as Glare-Free High Beam and marker light on the quality of object detection and possible alternatives such as symbol projection with structured light. The concept achieves a similar quality of object detection as conventional high beam, but requires only around 16% of the energy. This shows the energy saving potential of automated vehicles in combination with novel headlight control strategies.

Detecting and learning city intersection traffic contexts for autonomous vehicles

According to Business Wire, the global market for Autonomous Vehicles estimated at 6.1 thousand Units fpsin the year 2020, is projected to reach a revised size of 110.1 thousand Units by 2026, growing at a CAGR of 60.6% over the analysis period. This strong demand brings more research interests in autonomous vehicle systems. One of the hot topics is autonomous machine vision system and intelligent solutions. Most existing papers on autonomous vehicle machine vision systems apply machine learning models to address automatic object detection and classification issues to support automatic street traffic object detection and classification for vehicles, people/animal, traffic road signs, and signals. However, there is a lack of research results addressing automatic detection and classification of road contexts and transportation intersections under diverse weather conditions. In this work, we present an integrated machine learning model to address the issue and need in street intersection detection and classification based on road contexts and weather conditions. This paper reports our efforts in data collection, processing, and training based on existing data sets (such as BDD100k and COCO), and add a new training data set on street contexts and intersections (13 classes) and weather conditions (6 classes). This paper proposes a 2-stage integrated model to support the detection and classification of different types of traffic contexts and transportation intersection. In the first stage, two deep learning models (Yolo4 and Mask CNN) with transfer learning technique are used to detect the traffic signs, traffic lights, crosswalk and road direction targets on the road in addition to mobile objects (such as people and cars). Later, the generated results from the first stage are used as inputs to a decision tree model to detect and classify the different types of underlying transportation intersections. According to presented experimental results, the proposed 2-stage model receives a high accuracy, so it has strong potential application in computer vision technology of autonomous driving.

Automatic detection of foreign objects and contaminants in colonoscopic video data using deep learning

Abstract Introduction: In the field of colonoscopy results of automated or supporting procedures using medical image processing are often compromised by contaminants such as blood or stool, but also due to medical instruments. Such objects or contaminations often obscure relevant areas or cause disturbances in various algorithms and also prevent physicians from recognizing relevant information in the video-stream. Hence, automatic detection and segmentation of such contaminations is desirable, by obtaining proper segmentation masks to an algorithm yields the possibility to exclude such areas from analysis. Method: We created a training and validation dataset by collecting (and annotating, where necessary) endoscopic video sequences from various public available datasets and a privat collection. An encoder-decoder deep convolutional neural network was trained with this data to predict segmentation masks for blood, stool, medical instruments and other objects. Results: Depending on the class (blood, stool, ... ), we were able to obtain a mean Dice score in the range between 0.88 to 0.93 on the evaluation dataset. Conclusion: The proposed approach hence allows us providing segmentation masks that are able to reduce problems in the subsequent image analysis and also find regions that were not properly observed.

RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images

Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS-YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive and negative samples in training, we used the Varifocal Loss function. Finally, to obtain a high-performance remote sensing object detector, we combined the trained model with an open-source framework called Slicing Aided Hyper Inference (SAHI). This work evaluated models on three aerial remote sensing datasets (DOTA-v1.5, TGRS-HRRSD, and RSOD). Our comparative experiments demonstrate that our model has the highest accuracy in detecting objects in remote sensing image datasets.

Automatic recognition of parasitic products in stool examination using object detection approach.

BackgroundObject detection is a new artificial intelligence approach to morphological recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking. Based on the literatures, the YOLOv4-Tiny produces faster results and uses less memory with the support of low-end GPU devices. In comparison to the YOLOv3 and YOLOv3-Tiny models, this study aimed to propose an automated object detection approach, specifically the YOLOv4-Tiny model, for automatic recognition of intestinal parasitic products in stools.MethodsTo identify protozoan cysts and helminthic eggs in human feces, the three YOLO approaches; YOLOv4-Tiny, YOLOv3, and YOLOv3-Tiny, were trained to recognize 34 intestinal parasitic classes using training of image dataset. Feces were processed using a modified direct smear method adapted from the simple direct smear and the modified Kato-Katz methods. The image dataset was collected from intestinal parasitic objects discovered during stool examination and the three YOLO models were trained to recognize the image datasets.ResultsThe non-maximum suppression technique and the threshold level were used to analyze the test dataset, yielding results of 96.25% precision and 95.08% sensitivity for YOLOv4-Tiny. Additionally, the YOLOv4-Tiny model had the best AUPRC performance of the three YOLO models, with a score of 0.963.ConclusionThis study, to our knowledge, was the first to detect protozoan cysts and helminthic eggs in the 34 classes of intestinal parasitic objects in human stools.

Driver awareness collision/proximity detection system for heavy vehicles based on deep neural network

Heavy vehicles are essentially used to perform several critical tasks in construction industry. These heavy vehicles are different in shape and structure according to their capabilities and needs on the construction sites. Bigger size and different design of these vehicles result in extended blind spot compare to the normal vehicles, thus risking the equipment and workers operating around them. Each year, a large number of incidents are reported involving human injuries or loss of life due to improper handling of blind spots in heavy vehicles. In this paper we developed a deep learning based collision detection and driver assistance methodology by removing the blind spots in haul truck. In the proposed methodology, vision sensor data is used for automatic detection and classification of objects, employing the YOLO-v5 network architecture. For distance measurement of the objects we used stereo imaging camera Intel RealSense D455. The proposed algorithm detects and classify the object around the vehicle and provide the captioning to describe the nature of object to the driver. In addition, the algorithm also measures the distance of objects from the vehicle. The experimental results validate the performance of our proposed methodology in real time.

Creating Computer Vision Models for Respiratory Status Detection.

This study aims to use computers to detect and recognize ventilation objects (masks and tubes) and their positions on the patient's face. We created two models: the You Only Look Once (YOLO) and the Transfer Learning (TL) models, to perform this computer vision task. The development processes and comparison of performance will be described in this paper. The TL model had a better performance (93%) compared to the YOLO model (93%). Clinical Relevance- Healthcare providers and researchers interested in the field of computer vision applied in medicine, specifically automatic object detection using video streams or real-time video streaming may benefit from findings reported.

TAU: A framework for video-based traffic analytics leveraging artificial intelligence and unmanned aerial systems

Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers’ safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high-resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. These insights facilitate the traffic understanding using curves, histograms, heatmaps, and animations. The present work presents a valuable added value for academic researchers and transportation engineers to automate the traffic video analytics process and extract useful insights to optimize traffic performance. TAU is a ready-to-use framework for any Transportation Engineer to better understand and manage daily road traffic (video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI). The source code is available at: https://github.com/bilel-bj/TAU.

Military Target Detection in Remote Sensing Imagery Based on YOLOv4-Faster

The continuing development of remote sensing has resulted in a rapidly increasing number of remote sensing applications. High-resolution remote sensing images are used in various fields in the military. We propose methods for object detection based on remote sensing images. We develop a signal processing method for normalizing remote sensing images to eliminate noise such as fog, haze, and poor lighting. This method further improves detection accuracy and reduces error rates. We develop YOLOv4-faster, an accelerated neural network model based on the YOLO (You-Only-Look-Once) object detection method. YOLOv4-faster outperforms existing networks in terms of execution time and detection performance. We conduct a series of experiments on two public datasets (TGRS-HRRSD and NWPU VHR-10) as well as a dataset containing six military target classes provided by IMINT & Analysis and collected from Google Earth. YOLOv4-faster improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show improved mAP (mean average precision) performance of the proposed method for object detection in remote sensing images. We thus propose a novel system for automatic object detection for high-resolution remote sensing images.

Detection of anomalies in the proximity of a railway line: A case study

A point cloud describing a railway environment is considered in a case study aimed at presenting a workflow for the automatic detection of external objects that, coming too close to the railway infrastructure, may cause potential risks for its correct functioning. The approach combines classical semantic segmentation methodologies with a novel geometric and numerical procedure to define a region of interest, consisting of a lower tube enveloping the 3D space occupied by the train during its transit and an upper tube enclosing the overhead contact lines. One useful application could be automatic vegetation monitoring in the proximity of the railway structure, which would help with planning maintenance pruning activities.

Waste and Vehicle Detection Using YOLO

Abstract: Automatic detection and classification of objects is an important functionality of image analysis. Due to the nature and size of objects and the varied visual features, it becomes challenging to detect and classify objects in aerial images. Manual detection of objects in these images is very time consuming due to the nature and that data captured in these images. It is desirable to automate the detection of various features or objects from these images. The conventional methods for object classification involve two stages: (i) Identify the regions with object presence in the image and (ii) Classify the objects in the regions. Additionally, detection of objects becomes challenging in presence of complexities in background, size, noise, and distance parameters. This work proposes a customized YOLO to detect and classify different objects such as garbage waste, plastic waste and vehicles in the images.

WINDMILLS DETECTION USING DEEP LEARNING ON SENTINEL SATELLITE IMAGES

Abstract. Automatic detection of objects from Earth Observation images is still a challenge for researchers. This paper aims at extracting automatically windmills on mid-resolution images (10-meter resolution), based on Sentinel satellite products. Sentinel-2 optical images are obvious candidates for our study. At 10-meter resolution, a windmill is represented with only a few pixels. We also start to consider Synthetic Aperture Radar (SAR) Sentinel-1 images but no particular windmill radar response on GRD (Ground Range Multi-look Detected) products seemed to be relevant.Considering the maturity of deep learning techniques for object detection in computer vision, we explore the use of deep neural networks for windmill detection on remote sensing images. For that purpose, we had to create the training data sets but we took advantage of the availability of many Sentinel images and of the use of automated labelling as the objects are georeferenced. The proposed approach relies on the U-Net framework, reformulating our problem of object detection in terms of semantic segmentation. We trained several neural networks on different data sets emanating from different countries. That enabled us to measure the performance of detection within a country but also across two countries (training on a country and predicting on another country). The results show the ability of detection of such small objects with respect to the resolution and we obtain various levels of performances depending on the trained and test data sets.

Deep-Learning-Incorporated Augmented Reality Application for Engineering Lab Training

Deep learning (DL) algorithms have achieved significantly high performance in object detection tasks. At the same time, augmented reality (AR) techniques are transforming the ways that we work and connect with people. With the increasing popularity of online and hybrid learning, we propose a new framework for improving students’ learning experiences with electrical engineering lab equipment by incorporating the abovementioned technologies. The DL powered automatic object detection component integrated into the AR application is designed to recognize equipment such as multimeter, oscilloscope, wave generator, and power supply. A deep neural network model, namely MobileNet-SSD v2, is implemented for equipment detection using TensorFlow’s object detection API. When a piece of equipment is detected, the corresponding AR-based tutorial will be displayed on the screen. The mean average precision (mAP) of the developed equipment detection model is 81.4%, while the average recall of the model is 85.3%. Furthermore, to demonstrate practical application of the proposed framework, we develop a multimeter tutorial where virtual models are superimposed on real multimeters. The tutorial includes images and web links as well to help users learn more effectively. The Unity3D game engine is used as the primary development tool for this tutorial to integrate DL and AR frameworks and create immersive scenarios. The proposed framework can be a useful foundation for AR and machine-learning-based frameworks for industrial and educational training.

Automatic Object Detection and Direction Prediction of Unmanned Vessels Based on Multiple Convolutional Neural Network Technology

This study aims to detect objects quickly and accurately and then start the necessary obstacle avoidance procedure when the uncrewed vessel is at sea. This study uses a multivariate convolutional neural network (CNN) to perform automatic object detection and direction prediction for uncrewed vessels. This study is divided into three parts for processing. The first part of this process uses camera calibration technology to correct the image. Discrete cosine transform (DCT) is then used to detect sea level. Finally, this study uses Kalman filtering and affine transformation to stabilize images taken by uncrewed vessels at sea. The second part of the processing system uses a CNN to detect sea objects automatically. The third part of the process uses the dual-lens camera installed on the vessel to detect the distance and direction of objects at sea. In the experiment, the detection rate can reach more than 91% in this study method. In the experiment on image stabilization, this study’s method can also effectively improve video instability. In the experiment involving the distance and direction of the object, the experimental results show that the distance and direction of the object obtained by this study method have a distance error value of less than 10%, and the prediction results have a good effect no matter whether the object is at a short or long distance. It is hoped that this paper’s method can be applied to the automatic obstacle avoidance of unmanned vessels.

Review of Machine Learning Algorithms for Automatic Detection of Underground Objects in GPR Images

Ground-penetrating radar (GPR) is a nondestructive tool that has gained popularity after giving promising results in different areas—such as utility engineering, transportation engineering, civil engineering, and geology—with relatively low cost. Even as the number of applications for GPR increases, the interpretation of GPR data is still challenging, in part due to varying ground conditions. Researchers are continuously working on the development of new analysis methods to address these challenges. Computer vision algorithms, including neural networks and convolution neural networks, have advanced significantly over the past decade, and researchers have utilized these algorithms to extract information from GPR images and thus improve the interpretation of GPR data. This paper presents a review of literature that employs computer vision and machine learning algorithms, such as YOLO V3, Viola–Jones, and AlexNet, for automatic extraction of information from GPR images. The uptake in the use of automatic detection algorithms for GPR is increased by the ability to rapidly quantify and locate buried targets that previously could only be identified by professionals with a high level of expertise and training.

Automated Object Detection in Experimental Data Using Combination of Unsupervised and Supervised Methods.

Deep neural networks (DNN) have shown their success through computer vision tasks such as object detection, classification, and segmentation of image data including clinical and biological data. However, supervised DNNs require a large volume of labeled data to train and great effort to tune hyperparameters. The goal of this study is to segment cardiac images in movie data into objects of interest and a noisy background. This task is one of the essential tasks before statistical analysis of the images. Otherwise, the statistical values such as means, medians, and standard deviations can be erroneous. In this study, we show that the combination of unsupervised and supervised machine learning can automatize this process and find objects of interest accurately. We used the fact that typical clinical/biological data contain only limited kinds of objects. We solve this problem at the pixel level. For example, if there is only one object in an image, there are two types of pixels: object pixels and background pixels. We can expect object pixels and background pixels are quite different and they can be grouped using unsupervised clustering methods. In this study, we used the k-means clustering method. After finding object pixels and background pixels using unsupervised clustering methods, we used these pixels as training data for supervised learning. In this study, we used logistic regression and support vector machine. The combination of the unsupervised method and the supervised method can find objects of interest and segment images accurately without predefined thresholds or manually labeled data.

Fast and accurate concealed dangerous object detection for millimeter-wave images

Millimeter-wave (MMW) imaging is a touch-free method for security inspection at railway stations and airports. However, automatic detection of dangerous objects in MMW images is challenging; the characteristics of images are remarkable with variable appearances of an object, different contrasts, and lower resolutions. We propose a fast and accurate concealed dangerous object detection method called MMW image Detection (MMWDet). MMWDet comprises two stages: single-image detection and multi-angle image detection. In the single-image detection stage, we design a more robust feature fusion relationship between adjacent feature maps in a feature pyramid network to achieve a more robust feature representation of an object. Meanwhile, we employ localization confidence rather than category confidence in the classification branch to address the negative sample interference in the training process. Then, multi-angle image detection is proposed to reduce the false and missed detection rate based on a single image by refining the detection results of multi-angle images, which maps and filters the prediction boxes frame by frame based on matching pairs of adjacent image feature points. We collected a MMW dataset that included 38k images. Extensive experiments on the MMW dataset demonstrate the effectiveness and improved performance of the proposed method compared with advanced general detection methods.

Deep Learning for Archaeological Object Detection on LiDAR: New Evaluation Measures and Insights

Machine Learning-based workflows are being progressively used for the automatic detection of archaeological objects (intended as below-surface sites) in remote sensing data. Despite promising results in the detection phase, there is still a lack of a standard set of measures to evaluate the performance of object detection methods, since buried archaeological sites often have distinctive shapes that set them aside from other types of objects included in mainstream remote sensing datasets (e.g., Dataset of Object deTection in Aerial images, DOTA). Additionally, archaeological research relies heavily on geospatial information when validating the output of an object detection procedure, a type of information that is not normally considered in regular machine learning validation pipelines. This paper tackles these shortcomings by introducing two novel automatic evaluation measures, namely ‘centroid-based’ and ‘pixel-based’, designed to encode the salient aspects of the archaeologists’ thinking process. To test their usability, an experiment with different object detection deep neural networks was conducted on a LiDAR dataset. The experimental results show that these two automatic measures closely resemble the semi-automatic one currently used by archaeologists and therefore can be adopted as fully automatic evaluation measures in archaeological remote sensing detection. Adoption will facilitate cross-study comparisons and close collaboration between machine learning and archaeological researchers, which in turn will encourage the development of novel human-centred archaeological object detection tools.