Fast Object Research Articles

QCMOS Detectors and the Case of Hypothetical Primordial Black Holes in the Solar System, near Earth Objects, Transients, and Other High-cadence Observations

Abstract Recent progress with CMOS detector development has opened new parameter space for high cadence time resolved imaging of transients and fast proper motion solar system objects. Using computer simulations for a ground-based 1.23 m telescope, this research note illustrates the gain of a new generation of fast readout low noise qCMOS sensors over CCDs and makes the case for high precision monitoring of asteroid orbits that can potentially shed light on the hypothetical existence of low mass primordial black holes, as well as for other applications requiring high speed imaging.

Traffic Congestion Detection Using YOLOv8 Algorithm With CCTV Data

Community development and growth according to data from the Central Java Statistics Agency regarding the number of vehicles in Central Java Province in 2021 is 20 320 743. The increasing growth of society has caused vehicle density which is a serious problem in urban areas. This study developed a congestion detection system using the YOLOv8 algorithm to analyze traffic density from CCTV footage. Automated detection of traffic congestion is a critical challenge in urban transport management. YOLOv8, a fast and accurate object detection algorithm, is used to identify vehicles and count their number in various areas of the highway. This information is then processed to assess road congestion conditions, with the aim of detecting congestion. The data obtained were tested on two road scenarios and traffic conditions to evaluate the performance of the system. The results showed that the proposed system was able to detect congestion with an accuracy level of 59.2% from several experiments. The use of YOLOv8 enables real-time detection with efficient computing resources, making it a potential solution for large-scale deployment. This research shows that the incorporation of advanced object detection algorithms such as YOLOv8 with CCTV data can provide an effective solution for traffic management in large cities. This system is expected to improve response to congestion, help control traffic, and reduce the negative impact of congestion in urban areas

APLIKASI PENERJEMAH BAHASA ISYARAT BISINDO MENGGUNAKAN METODE YOLOv5 BERBASIS MOBILE

This research aims to develop a mobile application that is able to recognize and translate BISINDO in real-time using the YOLOv5 method. YOLOv5 (You Only Look Once version 5) is an efficient and fast object detection algorithm, making it suitable for implementation on mobile devices. In developing this application, we collected and labeled a typical BISINDO dataset, then trained the YOLOv5 model to detect and recognize these characteristics. The test results show that the implemented model has high accuracy in recognizing various BISINDO characteristics. Apart from that, this application is also designed with an intuitive and easy-to-use user interface, so that users can quickly and easily communicate with people using sign language. With this application, it is hoped that it can expand communication access for people with hearing impaired and increase their social inclusion in society. Further research is needed to continue improving the app's accuracy and efficiency and adding more features that support different user needs.

Dynamic phase measurement based on two-step phase-shifting interferometry with geometric phase grating

Abstract This paper proposes a novel dynamic 2-step phase-shifting interferometry method with a geometric phase grating and direct aberration-free object phase recovery algorithm. By exploring the amplitude-phase modulation property of the geometric phase grating, two-step phase-shifting interferograms with π / 2 phase shift can be obtained in a single shot through two kinds of spatial-multiplexing arrangements. Then the restriction on the background uniformity of the 2-step phase-shifting algorithms and the system aberration can be avoided simultaneously, based on the presented direct and fast object phase demodulation strategy with an object-free state recorded in advance, so an accurate object measurement result can be guaranteed. Accordingly, a detailed analysis of the theoretical model of error sources is conducted by taking the depolarization error of geometric phase grating and phase measurement model error into consideration, with a Least Squares Iteration optimization strategy established to further improve the measurement accuracy. Experiments on various types of phase objects, such as a photo-etched quartz substrate, the spatial light modulator, and a silicon chip, validate the effectiveness and accuracy of our method when compared with the reference phase obtained from 4-step temporal phase-shifting method. The dynamic phase measurement capability is demonstrated by exhibiting the evaporation process of alcohol droplet.

Fast and robust object region segmentation with self-organized lattice Boltzmann based active contour method

Fast and robust object region segmentation with self-organized lattice Boltzmann based active contour method

A Fast and High-Accuracy Foreign Object Detection Method for Belt Conveyor Coal Flow Images with Target Occlusion.

Foreign objects in coal flow easily cause damage to conveyor belts, and most foreign objects are often occluded, making them difficult to detect. Aiming at solving the problems of low accuracy and efficiency in the detection of occluded targets in a low-illumination and dust fog environment, an image detection method for foreign objects is proposed. Firstly, YOLOv5s back-end processing is optimized by soft non-maximum suppression to reduce the influence of dense objects. Secondly, SimOTA label allocation is used to reduce the influence of ambiguous samples under dense occlusion. Then, Slide Loss is used to excavate difficult samples, and Inner-SIoU is used to optimize the bounding box regression loss. Finally, Group-Taylor pruning is used to compress the model. The experimental results show that the proposed method has only 4.20 × 105 parameters, a computational amount of 1.00 × 109, a model size of 1.20 MB, and an mAP0.5 of up to 91.30% on the self-built dataset. The detection speed on the different computing devices is as high as 66.31, 41.90, and 33.03 FPS. This proves that the proposed method achieves fast and high-accuracy detection of multi-layer occluded coal flow foreign objects.

Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather

Fast and accurate object detection in foggy weather is crucial for visual tasks such as autonomous driving and video surveillance. Existing methods typically preprocess images with enhancement techniques before the object detector, so that the real-time performance of object detection decreases to some extent. Meanwhile, many popular object detection models rely solely on visual features for localization and classification. When fog is present, visual features would be so adversely impacted that the detection accuracy sharply decreases. Therefore, we propose an end-to-end prior knowledge-guided network called DR-YOLO for object detection in foggy weather. DR-YOLO integrates the atmospheric scattering model and the co-occurrence relation graph as prior knowledge into the entire training process of the detector. Firstly, Restoration Subnet Module (RSM) is designed to employ the atmospheric scattering model to guide the learning direction of the detector for dehazing features. Specifically, it is only adopted during the training process and does not increase the time cost of detection process. Secondly, for guiding the detector to pay more attention to potential co-occurring objects in the same scene, we introduce Relation Reasoning Attention Module (RRAM) that utilizes the co-occurrence relation graph to supplement deficient visual features in foggy weather. In addition, DR-YOLO employs Adaptive Feature Fusion Module (AFFM) to effectively merge the key features from the backbone and neck for the needs of RRAM and RSM. Finally, we conduct experiments on clear, synthetic and real-world foggy datasets to demonstrate the effectiveness of DR-YOLO. The source code is available at https://github.com/wenxinss/DR-YOLO.

AUTOMATED SYNTHESIS OF FAST LENSES FOR MODERN NIGHT VISION DEVICES

This article is devoted to research of the possibilities of automated design of fast and high-quality multi-lens objectives intended for use in night vision devices with modern image intensifier tubes of generation 4 and 4+, which have photocathodes with a diameter of up to 18 mm. Such image intensifier tubes are characterized by extended spectral sensitivity, high signal-to-noise ratio, and increased resolution. The computer program PODIL, developed earlier for the automated design of arbitrary optical systems, was applied to find out prescription data of fast lenses. For the parametric synthesis of optical systems, the modified evolutionary algorithm implemented in the program was used. The article presents the general methodology and results of automated parametric synthesis of a series of optical systems of objectives with different focal lengths, which contain from 7 to 9 lenses with spherical surfaces. The synthesized lenses provide angular field of view of 10°, 37° and 60° without any vignetting of axial and peripheral beams. The correspondent F numbers are 1.5, 1.33, and 1.4. The working spectral range of the designed lenses is determined by the spectral sensitivity of the photocathodes of the image intensifier tubes. In this research, it covers wavelengths from 0.45 to 0.9 μm. The longitudinal size of the designed optical systems (i.e. the distance from the first optical surface to the photocathode’s plane) is found within the range from 80 to 106 mm. The presented results of aberration analysis confirm the high image quality of the obtained optical systems. In particular, the maximum spatial frequencies of the evaluated polychromatic diffraction modulation transfer functions of the lenses over the entire field in both meridional and sagittal planes exceed 50 mm-1. The maximum values of relative distortion of the lenses are obtained within the range from 1% to 2%.

Environmentally adaptive fast object detection in UAV images

Detecting objects in aerial images poses a challenging task due to the presence of numerous small objects and complex environmental information. To address these problems, we propose an efficient detector specifically designed for aerial images, named EAF-YOLOv8, based on YOLOv8-S. In this paper, we introduce a novel backbone network called EAFNet, specifically designed for small object detection. EAFNet consists of the Rapidly Merging Receptive Fields Aggregation Module (RMRFAM) and Multi-Scale Channel Attention (MSCA). The RMRFAM utilizes dilated convolution (DConv) and partial convolution (PConv) to acquire richer receptive fields, capturing more extensive contextual information at higher levels while reducing redundancy in channel information, thereby accelerating inference speed. Furthermore, inspired by denoising tasks, we focus on the feature information surrounding the target background and propose MSCA. MSCA integrates channel attention with an embedded self-attention feature pyramid, extending the feature learning scope to the surrounding environment of the target, beyond the target itself. This approach utilizes enhanced background features to elicit a higher response for small targets, reducing false positives. Experimental results demonstrate that in UAVDT and VisDrone2019, the proposed EAF-YOLOv8 achieves mAP50 scores of 34.3% and 49.7%, respectively. Additionally, EAF-YOLOv8 exhibits high real-time inference speeds of 77.60 FPS and 55.56 FPS, showcasing competitive detection performance.

Mid-infrared computational temporal ghost imaging

Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow photodetector. The technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal object. However, the implementation of temporal ghost imaging necessitates ultrafast detectors or modulators for measuring or pre-programming the probing intensity patterns, which are not available in all spectral regions especially in the mid-infrared range. Here, we demonstrate a frequency downconversion temporal ghost imaging scheme that enables to extend the operation regime to arbitrary wavelengths regions where fast modulators and detectors are not available. The approach modulates a signal with temporal intensity patterns in the near-infrared and transfers the patterns to an idler via difference-frequency generation in a nonlinear crystal at a wavelength where the temporal object can be retrieved. As a proof-of-concept, we demonstrate computational temporal ghost imaging in the mid-infrared with operating wavelength that can be tuned from 3.2 to 4.3 μm. The scheme is flexible and can be extended to other regimes. Our results introduce new possibilities for scan-free pump-probe imaging and the study of ultrafast dynamics in spectral regions where ultrafast modulation or detection is challenging such as the mid-infrared and THz regions.

Robust Forest Fire Detection Method for Surveillance Systems Based on You Only Look Once Version 8 and Transfer Learning Approaches

Forest fires have emerged as a significant global concern, exacerbated by both global warming and the expanding human population. Several adverse outcomes can result from this, including climatic shifts and greenhouse effects. The ramifications of fire incidents extend widely, impacting human communities, financial resources, the natural environment, and global warming. Therefore, timely fire detection is essential for quick and effective response and not to endanger forest resources, animal life, and the human economy. This study introduces a forest fire detection approach utilizing transfer learning with the YOLOv8 (You Only Look Once version 8) pretraining model and the TranSDet model, which integrates an improved deep learning algorithm. Transfer Learning based on pre-trained YoloV8 enhances a fast and accurate object detection aggregate with the TranSDet structure to detect small fires. Furthermore, to train the model, we collected 5200 images and performed augmentation techniques for data, such as rotation, scaling, and changing due and saturation. Small fires can be detected from a distance by our suggested model both during the day and at night. Objects with similarities can lead to false predictions. However, the dataset augmentation technique reduces the feasibility. The experimental results prove that our proposed model can successfully achieve 98% accuracy to minimize catastrophic incidents. In recent years, the advancement of deep learning techniques has enhanced safety and secure environments. Lastly, we conducted a comparative analysis of our method’s performance based on widely used evaluation metrics to validate the achieved results.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner’s reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation

In this paper, we introduce a novel fast object detection framework, designed to meet the needs of real-time applications such as autonomous driving and robot navigation. Traditional processing methods often trade-off between accuracy and processing speed. To address this issue, we propose a hybrid data representation method that combines the computational efficiency of voxelization with the detail capture capability of direct data processing to optimize overall performance. Our detection framework comprises two main components: a Rapid Region Proposal Network (RPN) and a Refinement Detection Network (RefinerNet). The RPN is used to generate high-quality candidate regions, while the RefinerNet performs detailed analysis on these regions to improve detection accuracy. Additionally, we have implemented a variety of network optimization techniques, including lightweight network layers, network pruning, and model quantization, to increase processing speed and reduce computational resource consumption. Extensive testing on the KITTI and the NEXET datasets has proven the effectiveness of our method in enhancing the accuracy of object detection and real-time processing speed. The experimental results show that, compared to existing technologies, our method performs exceptionally well across multiple evaluation metrics, especially in meeting the stringent requirements of real-time applications in terms of processing speed.

ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection

Fast stereo based 3D object detectors have made great progress recently. However, they suffer from the inferior accuracy. We argue that the main reason is due to the poor geometry-aware feature representation in 3D space. To solve this problem, we propose an efficient stereo geometry network (ESGN). The key in our ESGN is an efficient geometry-aware feature generation (EGFG) module. Our EGFG module first uses a stereo correlation and reprojection module to construct multi-scale stereo volumes in camera frustum space, second employs a multi-scale bird&#x2019;s eye view (BEV) projection and fusion module to generate multiple geometry-aware features. In these two steps, we adopt deep multi-scale information fusion for discriminative geometry-aware feature generation, without any complex aggregation networks. In addition, we introduce a deep geometry-aware feature distillation scheme to guide stereo feature learning with a LiDAR-based detector. The experiments are performed on the classical KITTI dataset. On KITTI test set, our ESGN outperforms the fast state-of-art-art detector YOLOStereo3D by 5.14% on mAP_3<i>d</i> at 62<i>ms</i>. To the best of our knowledge, our ESGN achieves a best trade-off between accuracy and speed. We hope that our efficient stereo geometry network can provide more possible directions for fast 3D object detection.

Distillation embedded absorbable pruning for fast object re-identification

Combining knowledge distillation (KD) and network pruning (NP) shows promise in learning a light network to accelerate object re-identification. However, KD requires an untrained student network to establish more critical connections in early epochs, but NP demands a well-trained student network to avoid destroying critical connections. This presents a dilemma, potentially leading to a collapse of the student network and harming object Re-ID performance. For that, we propose a distillation embedded absorbable pruning (DEAP) method. We design a pruner-convolution-pruner (PCP) unit to resolve the dilemma by loading NP’s sparse regularization on extra untrained pruners. Additionally, we propose an asymmetric relation knowledge distillation method. It readily transfers feature representation knowledge and asymmetric pairwise similarity knowledge without using additional adaptation modules. Finally, we apply re-parameterization to absorb pruners of PCP units to simplify student networks. Experiments demonstrate the superiority of DEAP, such as on the VeRi-776 dataset, with ResNet-101 as a teacher, DEAP saves 73.24% of model parameters and 71.98% of floating-point operations without sacrificing accuracy.

Non-Adaptive and Adaptive Two-Sided Search with Fast Objects

In 1946, Koopman introduced a two-sided search model. In this model, a searched object is active and can move, at most, one step after each test. We analyze the model of a combinatorial two-sided search by allowing more moves of the searched object after each test. We give strategies and show that they are optimal. We consider adaptive and non-adaptive strategies. We show the surprising result that with the combinatorial two-sided search on a path graph, the optimal non-adaptive search needs the same number of tests as the corresponding adaptive strategy does. The strategy obtained can also be used as an encoding strategy to sent the position of a moving element through a transmission channel.

Development of Annulus-Object Random Bin Picking System based on Rapid Establishment of RGB-D Images

With the development of the automation industry, robotic arms, and vision applications are no longer limited to fixed actions of the past. Production lines increasingly require the recognition and grasping of objects in complex environments, emphasizing quick setup and stability. In this paper, a rapidly constructed eye-hand system for robotic arm grasping, which enables fast and efficient object manipulation, particularly for stacking, is introduced. Initially, images were captured using a camera to generate extensive datasets from a limited number of images. Objects were subsequently segmented and categorized using deep learning networks for object detection and instance segmentation. Three-dimensional position information was obtained from an RGB-D camera. Finally, object poses were determined based on plane normal vectors, and gripping positions were manually marked. This reduced the time required for grab-point identification, model training, and pose localization. Based on experimental results, the grasping procedure proposed in this paper is suitable for various object-grasping scenarios. It achieved impressive picking success rates of 96% for unstacked annular objects and 90.86% for random bin annular objects, respectively. In the final experiment, following depth information filtering, a success rate of 95.1% was attained with random bin annular object picking.

Study of methods for searching and localizing objects in images from aircraft using convolutional neural networks

The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement. Methods of object localization and classification using neural networks require a detailed study and research of the quality of their work on data that has certain specifics, such as vehicle detection. The use of neural networks to detect certain types of objects using images obtained from aircraft can also help in the study of hard-to-reach locations. Therefore, the main subject of this paper is the localization and classification of objects in images obtained using digital cameras mounted on aircraft. The main focus is on determining the accuracy of object localization and detection using selected types of neural networks, which are the most important indicators of neural network efficiency. The speed of a neural network is also an equally important characteristic as it directly affects its ability to be used in tasks that require fast object localization, such as video surveillance or automated car control systems. The main goal of this study is to study the accuracy of object localization and classification in images obtained with the help of cameras mounted on aircraft, as well as to study the speed of neural networks and determine the effectiveness of their application in real-world conditions. The objectives of this study are to train YOLO v5, SSD, and Faster RCNNs on the VisDrone dataset and to further study them on the vehicle localization dataset. The main goal of this work is to obtain statistics on the performance of neural networks trained on the VisDrone dataset. On the basis of the obtained statistics, conclusions are drawn about the effectiveness of the considered neural networks. The conclusions are drawn by considering the speed of the model, localization (IoU), and classification (Precision, Recall) metrics. Possible directions for further development of the topic under study are presented as conclusions.

Event-Based Non-rigid Reconstruction of Low-Rank Parametrized Deformations from Contours

Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras. In recent years, event cameras have gained significant attention due to their bio-inspired properties, such as high temporal resolution and high dynamic range. In this paper, we propose a novel approach for reconstructing such deformations using event measurements. Under the assumption of a static background, where all events are generated by the motion, our approach estimates the deformation of objects from events generated at the object contour in a probabilistic optimization framework. It associates events to mesh faces on the contour and maximizes the alignment of the line of sight through the event pixel with the associated face. In experiments on synthetic and real data of human body motion, we demonstrate the advantages of our method over state-of-the-art optimization and learning-based approaches for reconstructing the motion of human arms and hands. In addition, we propose an efficient event stream simulator to synthesize realistic event data for human motion.

Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor.

The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.