Object Detection

Uninterrupted power supply to electric power consumers has increasingly become a global necessity. Monitoring the health of distribution network is crucial to provide quality service. Traditional monitoring methods based on on-site patrols to detect faults have increasingly become labor-intensive and time-consuming, raising demand for new and more efficient techniques. To address this issue, we propose faster-RCNN by MXNet for both detection and classification tasks. We utilize convolutional neural network (CNN) for detecting and classifying both insulator components and faulty insulator discs from images captured on overhead electric power transmission systems. Using a dataset of images acquired through UAV (unmanned aerial vehicle) captures, detection and classification is dealt with by dividing the picture content of the training set into three classes: background, insulator and the defective part of insulator. We achieve target insulator recognition and positioning with impressive precision compared to other traditional technologies. Our work could have practical integrated implementation solutions for automated inspection of overhead transmission power line insulators.

Waste management is one of the major problems of the modern cities. Increasing the effectiveness of the recycling processes helps to reduce the amount of the waste needed to store, to improve the quality of life in the city, and to preserve economical values. Modern neural network-based object detection methods are used to automatize the recyclable waste segregation. In this paper, we proposed a novel waste localization and classification method that works on video streams of unconditional environments. The method uses a variant of Inception architecture and faster R-CNN algorithm to detect the recyclable items. Spatio-temporal information is used to enhance the classification accuracy on varying illumination and backgrounds by adopting an object tracking algorithm. The experiments showed that the proposed method achieves promising results on difficult scenes and different kinds of recyclable objects.

Nowadays, the possibilities offered by state-of-the-art deep neural networks allow the creation of systems capable of recognizing and indexing visual content with very high accuracy. Performance of these systems relies on the availability of high quality training sets, containing a large number of examples (e.g. million), in addition to the the machine learning tools themselves. For several applications, very good training sets can be obtained, for example, crawling (noisily) annotated images from the internet, or by analyzing user interaction (e.g.: on social networks). However, there are several applications for which high quality training sets are not easy to be obtained/created. Consider, as an example, a security scenario where one wants to automatically detect rarely occurring threatening events. In this respect, recently, researchers investigated the possibility of using a visual virtual environment, capable of artificially generating controllable and photo-realistic contents, to create training sets for applications with little available training images. We explored this idea to generate synthetic photo-realistic training sets to train classifiers to recognize the proper use of individual safety equipment (e.g.: worker protection helmets, high-visibility vests, ear protection devices) during risky human activities. Then, we performed domain adaptation to real images by using a very small image data set of real-world photographs. We show that training with the generated synthetic training set and using the domain adaptation step is an effective solution to address applications for which no training sets exist.

Wrong-way driving is one of the main causes of road accidents and traffic jam all over the world. By detecting wrong-way vehicles, the number of accidents can be minimized and traffic jam can be reduced. With the increasing popularity of real-time traffic management systems and due to the availability of cheaper cameras, the surveillance video has become a big source of data. In this paper, we propose an automatic wrong-way vehicle detection system from on-road surveillance camera footage. Our system works in three stages: the detection of vehicles from the video frame by using the You Only Look Once (YOLO) algorithm, track each vehicle in a specified region of interest using centroid tracking algorithm and detect the wrong-way driving vehicles. YOLO is very accurate in object detection and the centroid tracking algorithm can track any moving object efficiently. Experiment with some traffic videos shows that our proposed system can detect and identify any wrong-way vehicle in different light and weather conditions. The system is very simple and easy to implement.

This paper contains the details of different object detection (OD) techniques, object identification's relationship with video investigation, and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strategies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method (OFM). This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has been appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focuses comparing to the moving items. Next piece of denoting the necessary moving object of interest checks to the post-preparing. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing complex troupes which join numerous low-level picture highlights with significant level setting from object indicators and scene classifiers. With the fast advancement in profound learning, all the more useful assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in customary designs. These models carry on contrastingly in network design, preparing system, and advancement work, and so on in this review paper, we give an audit on profound learning-based item location systems. Our survey starts with a short presentation on the historical backdrop of profound learning and its agent device, in particular, Convolutional Neural Network (CNN) and region-based convolutional neural networks (R-CNN).

There have been a lot of developments towards the Humans Computers Interaction (HCI). Many modules have been developed to help the physical world interact with the digital world. Here, the proposed paper serves to be a new approach for controlling mouse movement using Colored object and marker motion tracking. The project mainly aims at mouse cursor movements and click events based on the object detection and marker identification. The software is developed in Python Language and OpenCV and PyAutoGUI for mouse functions. We have used colored object to perform actions such as movement of mouse and click events. This method mainly focuses on the use of a Web Camera to develop a virtual human computer interaction device in a cost effective manner.