A vision-based autonomous detection scheme for obstacles on the runway

In stereo vision, disparity map after stereo matching using projection model can get the depth information and 3D information of the original image. In order to improve the accuracy and speed of stereo matching, improving the quality of disparity map becomes the core problem of stereo matching. Firstly, the paper extracted edge information of binocular images using Canny, Scharr and Sobel edge detection algorithm. On this basis, the paper use SGBM algorithm(Semi-global block Matching) and DP algorithm(Dynamic Programming)and CSCA algorithm(Cross-scale Cost Aggregation) respectively calculated the disparity map and its error rate. After analysis, the combination of Canny detection algorithm and CSCA algorithm can eliminate a large number of number of irrelevant information and reduce the amount of data processing. The real scene image is selected to validate the method .Based on the above research results, the method improved the quality of disparity map.

This study proposed a model for highway accident detection that combines the You Only Look Once v3 (YOLOv3) object detection algorithm and Canny edge detection algorithm. It not only detects whether an accident has occurred in front of a vehicle, but further performs a preliminary classification of the accident to determine its severity. First, this study established a dataset consisting of around 4500 images mainly taken from the angle of view of dashcams from an open-source online platform. The dataset was named the Highway Dashcam Car Accident for Classification System (HDCA-CS) and was developed with the aim of conforming to the setting of this study. The HDCA-CS not only considers weather conditions (rainy days, foggy days, nighttime settings, and other low-visibility conditions), but also various types of accidents, thus increasing the diversity of the dataset. In addition, we proposed two types of accidents—accidents involving damaged cars and accidents involving overturned cars—and developed three different design methods for comparing vehicles involved in accidents involving damaged cars. Canny edge detection algorithm processed single high-resolution images of accidents were also added to compensate for the low volume of accident data, thereby addressing the problem of data imbalance for training purposes. Lastly, the results showed that the proposed model achieved a mean average precision (mAP) of 62.60% when applied to the HDCA-CS testing dataset. When comparing the proposed model with a benchmark model, two abovementioned accident types were combined to allow the proposed model to produce binary classification outputs (i.e., non-occurrence and occurrence of an accident). The HDCA-CS was then applied to the two models, and testing was conducted using single high-resolution images. At 76.42%, the mAP of the proposed model outperformed the benchmark model’s 75.18%; and if we were to apply the proposed model to only test scenarios in which an accident has occurred, its performance would be even better relative to the benchmark. Therefore, our findings demonstrate that our proposed model is superior to other existing models.

Integration of image processing in order to detect red blood cells (RBCs) in human urine enables technology to reduce medical technician’s manual counting time and error factor. The general objective of this study was to detect and count the red blood cells in human urine using Canny Edge Detection (CED) and Circle Hough Transform (CHT) algorithms. CED is an edge detection algorithm used in order to identify a great variety of edges in an image. CHT is one of the features of the Hough Transform. Specifically, CHT is used in order to detect circular objects. The basis of the CHT operation will be dependent on the circular edges detected by the CED. In order to identify a specific circle size, the minimum and maximum radius must be set. Particularly, to differentiate the RBCs from other cells such as white blood cells. For this study, the minimum radius was 4 pixels while the maximum was 6. Compared to the manual counting of a medical technician, the automated counting of the device produced a percent error of 9.561% and an average counting time of 0.4561 seconds per sample.

Surface microseismic monitoring is widely used in hydraulic fracturing. Real‐time monitoring data collected during fracturing can be used to perform surface‐microseismic localization, which aids in assessing the effects of fracturing and provides guidance for the process. The accuracy of localization critically depends on the quality of monitoring data. However, the signal‐to‐noise ratio of the data is often low due to strong coherent and random noise, making denoising essential for processing surface monitoring data. To suppress noise more effectively, this paper introduces a novel denoising method that integrates the Ramanujan subspace with dynamic time warping and adaptive singular value decomposition. The new method consists of two steps: First, a Ramanujan subspace is constructed to suppress periodic noise. Then, dynamic time warping and adaptive singular value decomposition are applied to eliminate remaining coherent and random noise. The method has been evaluated using both synthetic and field data, and its performance is compared with traditional microseismic denoising techniques, including bandpass filtering and empirical mode decomposition.

Image edge detection significantly preserves the important structural properties in an image, while reducing the amount of data and filters out useless information. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper introduces the Canny edge detection algorithm and the implementation of the Canny edge-detection algorithm on an FPGA. Custom implementation in FPGAs, using a combination of hardware and software co-design, allows real-time processing, providing a good trade-off between performance and flexibility. The results show that FPGAs are good alternatives, which can be used to off-load these computationally-intensive and repetitive functions as co-processors and the capability of supporting canny edge detection for real-time image processing.

Adaptive Singular Value Decomposition (ASVD) is a coherency-based filter which decomposes data into its eigenimages and can detect horizontal events in the first eigenimages. By using the adaptive method, the ground roll is converted to a horizontal event in each window. By zeroing the first eigenvalues which present the ground roll, it can be suppressed. By increasing the number of eigenvalues to be zeroed, the ground roll is better attenuated but more signals are damaged as well. In this paper, an ASVD filter, in MATLAB code, was applied to synthetic data containing the ground roll, refractors and reflectors over an earth model with one weathering layer and a real shot record from the South West of Iran. Results show that the ASVD can attenuate the ground roll with minimum harm to the signals. The ASVD method has two important advantages: (1) it can calculate the velocity for the best rotation to make the ground roll as horizontal as possible in each window and (2) the results of applying the filter to the synthetic data with various signal-to-noise ratios (SNR) show that this filter is not sensitive to SNR.

The detection process of partial discharge (PD) ultra-high frequency (UHF) signal is easily affected by white noise and periodic narrowband noise, which hinder the fault diagnosis of high-voltage electrical appliances. In order to extract PD UHF signal and suppress noise effectively, this paper provides a new method to detect PD UHF signal by EDSSV and low rank RBF neural network. Firstly, the singular value decomposition (SVD) is performed on the mixed noises of PD signal. Secondly, the peak index of energy difference spectrum of singular value (EDSSV) is selected as optimal singular value threshold, and then the periodic narrowband noise is eliminated by reconstructing the effective rank order. Finally, radial basis function (RBF) neural network is used to approximate the denoised PD signal, and Gaussian window filter is used to extract the PD signal. To verify the performance of the proposed method, we compared it with other three algorithms in simulation and field detection, including adaptive singular value decomposition (ASVD), singular value decomposition based on S-transform and MTFM (S-SVD) and EMD-WT algorithms. Particularly, four evaluation indices are designed for the detection data, which consider both the noise suppression and feature preservation. The results demonstrate the validity of the proposed method with higher signal-to-noise ratio and less waveform distortion.

This paper presents a new automatic image segmentation method for segmenting moving object in complex environment by combining the motion information with edge information. We propose an adaptive optical flow method based on the Horn-Schunck algorithm to estimate the optical flow field. Our method puts different smoothness constraints on different directions and optical flow constraint is used according to the gradient magnitude. Canny edge detector can obtain the most edge information but miss some pixels. In order to restore these missing pixels the edge has a growing based on the continuity of optical flow field. Next, by remaining the block that has the longest edge could delete the noise in the background, and then the last segmentation result is obtained. The experimental result demonstrates that this method can segment the moving object in complex environment precisely.

In this paper, image smoothening, gradient magnitude, hystersis used in Canny edge detection algorithm is presented. The new algorithm used has a low-complexity 8-bin non-uniform gradient magnitude histogram to compute block-based hysteresis thresholds that are used by the Canny edge detector. This also achieves less memory requirements, decreased latency and increased throughput with no loss in edge detection. Furthermore, the hardware architecture of the proposed algorithm is presented in this paper and the architecture is synthesized on the Xilinx Virtex 5 FPGA. The design development is done in VHDL and simulated results are obtained using modelsim 6.3 with Xilinx 12.2.

Novel watermarking model is developed on this paper using laplacian filter from two previous watermarking models that use CRT algorithms. The first model is embedding the watermark on the random pixel on each single block which can degrade the homogeneous areas. The second model use the canny edge detection to determine the edges as embedding locations within binary representation without considering the gradient magnitude that causes less optimal result. The proposed model is developed from the laplacian filter to generate the gradient magnitude of the image regions as embedding locations. The higher gradient magnitude represents the better location for embedding. Bits of watermark are embedding from the location with the highest gradient magnitude to the lower gradient magnitudes according to the number of watermark bits. The proposed model has higher imperceptibility with average SSIM of 0.9996 than the conventional CRT with average SSIM of 0.9985. In compression test, the proposed model has higher robustness with average NC value of 0.6746 than the previous models that have NC average values of 0.6618 and 0.6616 correspondingly. Temporarily, in noise test, all of the models have the similar average NC values. The model has better visual quality of the watermarked image than the previous models that use block division and canny edge detection algorithm. It also has better robustness against compression.

As an important part of the automation industrial system, mechanical arm robot plays an important role in service, exploration, automation engineering and other fields, and has a broad application prospect. The traditional mechanical arm and Sobel edge detection methods have some shortcomings: 1. The PID method is separated from the ontology model and only carries out simple and multiple calculations according to the code of the steering engine, which is not stable enough and will cause more time consumption; 2. In some complex environments, while Sobel edge detection is detecting the shape of objects, the separation between subject and background is not clear, which makes it impossible to obtain accurate object information. Therefore, this paper proposes a new PID method and Canny edge detection algorithm: 1. PID method combined with three-dimensional coordinate system modeling, to achieve a more stable, faster and more robust calculation formula, to complete the mechanical arm grasping function; 2. Using Canny edge detection and three-dimensional coordinate system to analyze the object image to obtain the accurate information of the target object.

Rheumatoid arthritis (RA) is a long lasting autoimmune disorder that affects the multiple joints of human body. The aim and objective of the study was i) to implement the automated segmentation of knee x-ray image and thermal image using fuzzy c means and canny edge detection algorithm. ii) To compare both the imaging modalities by means of feature extraction and correlate with the biochemical method as standard. Fifteen subjects with RA in knee region and 15 healthy controls were included in this study. The segmentation of thermal images was performed using fuzzy c-means algorithm and x-ray segmentation was implemented using canny edge detection algorithm. The skin surface temperature weremeasured in the thermal image of knee regionin both RA and control subjects. The features wereextracted from the segmented region of the knee x-ray image. The automated segmentation implemented in thermal imaging provided better results compared to x-ray image segmentation process. The thermal imaging feature and x-ray imaging features correlated significantly with the standard parameters.

The purpose of this study was to investigate the ability of quantitative ultrasound (US) using edge detection analysis to assess patients with Duchenne muscular dystrophy (DMD). After Institutional Review Board approval, US examinations with fixed technical parameters were performed unilaterally in 6 muscles (biceps, deltoid, wrist flexors, quadriceps, medial gastrocnemius, and tibialis anterior) in 19 boys with DMD and 21 age-matched control participants. The muscles of interest were outlined by a tracing tool, and the upper third of the muscle was used for analysis. Edge detection values for each muscle were quantified by the Canny edge detection algorithm and then normalized to the number of edge pixels in the muscle region. The edge detection values were extracted at multiple sensitivity thresholds (0.01-0.99) to determine the optimal threshold for distinguishing DMD from normal. Area under the receiver operating curve values were generated for each muscle and averaged across the 6 muscles. The average age in the DMD group was 8.8 years (range, 3.0-14.3 years), and the average age in the control group was 8.7 years (range, 3.4-13.5 years). For edge detection, a Canny threshold of 0.05 provided the best discrimination between DMD and normal (area under the curve, 0.96; 95% confidence interval, 0.84-1.00). According to a Mann-Whitney test, edge detection values were significantly different between DMD and controls (P < .0001). Quantitative US imaging using edge detection can distinguish patients with DMD from healthy controls at low Canny thresholds, at which discrimination of small structures is best. Edge detection by itself or in combination with other tests can potentially serve as a useful biomarker of disease progression and effectiveness of therapy in muscle disorders.

An electrocardiogram (ECG) system deals with several challenges related with noise sources. The denoising process is a challenge due to ECG signal amplitude with respect to noise amplitude. The filtering process can be executed by analogue filters or digital filters, however, modern systems uses digital filters mainly because their flexibility, efficiency and hardware costs reduction. This paper compares the ability of two digital techniques used in ECG denoising, namely Adaptive Filter (AF) and Singular Value Decomposition (SVD). The techniques were applied to real ECG signals contaminated with the most common noise sources; artefacts caused by electromyography (EMG) and power line noise (Hum).

Obstacle detection by evaluation of optical flow fields from image sequences