DETECTION OF TRAFFIC DENSITY WITH IMAGE PROCESSING USING PIN HOLE ALGORITHM

The intelligent traffic monitors are devloped and became more interst in recent years. A detection system in the monitoring traffic system is proposed using different algorithms. Pin Hole Algorithm used to detect the car that passes the road (the studied area). A fixed camera mounted at predetermined point used with known height (of the camera), the intensity of the light, and the visibility of the camera. The classification process is important to know the traffic congestion status. The traffic congestion status will be sent to the server address already provided. In the congestion detection test results were obtained with an accuracy value of 85% using the 64x64 grid division and obtaining good detection results for susceptible light intensity values between 5430 and 41379 LUX with an accuracy value of between 60% and 90%. ABSTRAK: Sejak beberapa tahun ini, sistem pengawasan trafik pintar telah dibina dan terus berkembang luas. Sistem pengesanan dalam sistem trafik pengawasan telah dicadangkan menggunakan pelbagai algoritma. Algoritma lubang pin digunakan bagi mengesan kereta yang melalui jalan (kawasan kajian). Kamera dipasang tetap pada titik tertentu iaitu dengan menyelaras ketinggian kamera, keamatan cahaya, dan kebolehlihatan kamera. Proses klasifikasi sangat penting bagi menentukan status kesesakan trafik. Status kesesakan trafik akan dihantar ke alamat pelayan yang telah disediakan. Nilai ketepatan ujian pengesanan kesesakan yang diperoleh adalah 85% iaitu menggunakan pembahagi grid 64x64 dan dapatan kajian menunjukkan pengesanan yang baik bagi nilai keamatan cahaya antara 5430 dan 41379 LUX dengan nilai ketepatan antara 60% dan 90%.

A Novel Spectrophotometric Determination and Kinetic Study of Sulfamethoxazole in Pure and Tablet Formulation using 9-chloroacridine Reagent

A spectrophotometric method has been developed for analysis of Sulfamethoxazole (SMX) in pure and dosage forms. The method is based on the reaction of the SMX with 9-chloroacridine (9-CA) reagent in organic and acidic medium, to produce a yellow product having maximum absorption at 448 nm. Beer’s law was obeyed in the concentration range 1-30 μg.ml-1 with molar absorptivity of 1.63x104 L.mol-1.cm-1 with good detection and quantification limits. Accuracy (Average recovery %) and precision are 98.43% and 0.651, respectively. The proposed method was applied successfully for determination of Sulfamethoxazole in its commercial dosage form as tablet and agree well with the official method. The equilibrium constant and the thermodynamic functions (ΔHo, ΔGº and ΔSº) of the complex formation were estimated. The study revealed that the complex formation could occur spontaneously, the type of interacting forces between SMX and 9-CA are physical is nature and association increases the order of the studied systems. The results of kinetic parameters indicated that, the reaction is pseudo first order with respect to SMX. The rate constant at various temperatures and the thermodynamic functions of activation were determined. Theoretical parameters were calculated by applying the semi-empirical Austin method (AM1). These parameters are helped to suggest reaction mechanism and supporting other results.

Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning

Many nations are challenged by landscape fires. A confident knowledge of the area and distribution of burning is crucial to monitor these fires and to assess how they might best be reduced. Given the differences that arise using different detection approaches, and the uncertainties surrounding burned-area estimates, their relative merits require evaluation. Here we propose, illustrate, and examine one promising approach for Indonesia where recurring forest and peatland fires have become an international crisis. Drawing on Sentinel-2 satellite time-series analysis, we present and validate new 2019 burned-area estimates for Indonesia. The corresponding burned-area map is available at https://doi.org/10.5281/zenodo.4551243 (Gaveau et al., 2021a). We show that >3.11 million hectares (Mha) burned in 2019. This burned-area extent is double the Landsat-derived official estimate of 1.64 Mha from the Indonesian Ministry of Environment and Forestry and 50 % more that the MODIS MCD64A1 burned-area estimate of 2.03 Mha. Though we observed proportionally less peatland burning (31 % vs. 39 % and 40 % for the official and MCD64A1 products, respectively), in absolute terms we still observed a greater area of peatland affected (0.96 Mha) than the official estimate (0.64 Mha). This new burned-area dataset has greater reliability than these alternatives, attaining a user accuracy of 97.9 % (CI: 97.1 %–98.8 %) compared to 95.1 % (CI: 93.5 %–96.7 %) and 76 % (CI: 73.3 %–78.7 %), respectively. It omits fewer burned areas, particularly smaller- (<100 ha) to intermediate-sized (100–1000 ha) burns, attaining a producer accuracy of 75.6 % (CI: 68.3 %–83.0 %) compared to 49.5 % (CI: 42.5 %–56.6 %) and 53.1 % (CI: 45.8 %–60.5 %), respectively. The frequency–area distribution of the Sentinel-2 burn scars follows the apparent fractal-like power law or Pareto pattern often reported in other fire studies, suggesting good detection over several magnitudes of scale. Our relatively accurate estimates have important implications for carbon-emission calculations from forest and peatland fires in Indonesia.

A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism

Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

Identification of Face Mask With YOLOv4 Based on Outdoor Video

The use of face masks in the current era is one of the special regulations in many countries including Indonesia to prevent the spread of coronavirus. However, not all people strongly agree to wear masks because they feel uncomfortable to wear even in crowded places require the use of masks such as shopping malls, hospitals, factories, stations and others by checking manually. Therefore, in the study proposed automatic detection of masks with YOLOv4 with the stage of data collection recording community activities in crowded places, labeling images of masks and non masks. The labelling results were conducted in training that resulted in 90.3% accuracy in the 2000 ierasi, the last of which was video testing in three different crowd locations: taxes, city parks and highways. Based on the test results, YOLOv4 can detect masks and non masks on videos with different obstruction conditions such as people wearing helmets, hand obstacles. However, for the detection of people with tissue obstruction conditions and improper position of wearing masks has not resulted in good detection.

Practical comparation of the accuracy and speed of YOLO, SSD and Faster RCNN for drone detection

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN, YOLO, and SSD for effective drone detection in various environments. We have found that both Faster RCNN and YOLO have high recognition ability compared to SSD; on the other hand, SSD has good detection ability.

A Single Target Grasp Detection Network Based on Convolutional Neural Network

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

Typical Fluorescent Sensors Exploiting Molecularly Imprinted Hydrogels for Environmentally and Medicinally Important Analytes Detection

In this work, two typical fluorescent sensors were generated by exploiting molecularly imprinted polymeric hydrogels (MIPGs) for zearalenone (ZON) and glucuronic acid (GA) detection, via the analyte’s self-fluorescence property and receptor’s fluorescence effect, respectively. Though significant advances have been achieved on MIPG-fluorescent sensors endowed with superior stability over natural receptor-sensors, there is an increasing demand for developing sensing devices with cost-effective, easy-to-use, portable advantages in terms of commercialization. Zooming in on the commercial potential of MIPG-fluorescent sensors, the MIPG_ZON is synthesized using zearalanone (an analogue of ZON) as template, which exhibits good detection performance even in corn samples with a limit of detection of 1.6 μM. In parallel, fluorescein-incorporated MIPG_GA is obtained and directly used for cancer cell imaging, with significant specificity and selectivity. Last but not least, our consolidated application results unfold new opportunities for MIPG-fluorescent sensors for environmentally and medicinally important analytes detection.

Image detection and parameterization for different components in cross-sections of radial tires

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.