Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.National Science Foundation (U.S.) (grant No. 1414108)Eversource Energ

Line-based deep learning method for tree branch detection from digital images

The final publication is available at Elsevier via https://doi.org/10.1016/j.jag.2022.102759. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 licensePreventive maintenance of power lines, including cutting and pruning of tree branches, is essential to avoid interruptions in the energy supply. Automatic methods can support this risky task and also reduce time consuming. Here, we propose a method in which the orientation and the grasping positions of tree branches are estimated. The proposed method firstly predicts the straight line (representing the tree branch extension) based on a convolutional neural network (CNN). Secondly, a Hough transform is applied to estimate the direction and position of the line. Finally, we estimate the grip point as the pixel point with the highest probability of belonging to the line. We generated a dataset based on internet searches and annotated 1868 images considering challenging scenarios with different tree branch shapes, capture devices, and environmental conditions. Ten-fold cross-validation was adopted, considering 90% for training and 10% for testing. We also assessed the method under corruptions (gaussian and shot) with different severity levels. The experimental analysis showed the effectiveness of the proposed method reporting F1-score of 96.78%. Our method outperformed state-of-the-art Deep Hough Transform (DHT) and Fully Convolutional Line Parsing (F-Clip).This research was funded by CNPq (p: 433783/2018–4, 310517/2020–6, 314902/2018–0, 304052/2019–1 and 303559/2019–5), FUNDECT (p: 59/300. 066/2015, 071/2015) and CAPES PrInt (p: 88881.311850/2018–01). The authors acknowledge the support of the UFMS (Federal University of Mato Grosso do Sul) and CAPES (Finance Code 001). This research was also partially supported by the Emerging Interdisciplinary Project of Central University of Finance and Economics

Detección y geolocalización de objetos mediante visión artificial desde vehículos en movimiento

Las técnicas de la visión artificial han evolucionado y han ganado relevancia a lo largo de los últimos años dentro del ámbito tecnológico, implementándose en una gran variedad de campos, siendo uno de estos y el principal en el que será principalmente centrado este trabajo el de la conducción autónoma. En este proyecto se desarrollará un algoritmo capaz de realizar la detección y la geolocalización de objetos captados desde diferentes cámaras situadas en un vehículo en movimiento, haciendo uso de imágenes tomadas desde diferentes perspectivas, así como de diferentes metadatos que proporcionen la información necesaria para ubicar a los objetos dentro del recorrido realizado por el vehículo. Para llevar a cabo estos objetivos, será necesario el análisis y filtrado de una base de datos que proporcione toda la información necesaria para el uso del algoritmo, así como el estudio y aplicación de técnicas de triangulación mediante visión artificial para ser utilizadas por el algoritmo. Además de esto, se deberá diseñar e implementar el algoritmo que hará uso de los datos filtrados y las técnicas de triangulación previamente estudiadas, teniendo el objetivo final de medir la eficacia de su predicción y resultados con respecto a la posición real del objeto. Como resultado final, se obtendrá un algoritmo que en base a una información concreta introducida mediante ficheros en formato CSV, realizará el proceso de geolocalizar los objetos contenidos en los datos obteniendo su posición dentro del recorrido realizado por el vehículo. Además de estas coordenadas, el algoritmo realizará una representación gráfica con una perspectiva de vista aérea del escenario y los objetos que lo componen, representando la posición del vehículo en el momento en el que realizo cada una de las detecciones del objeto, así como la posición predicha por el algoritmo del objeto y su posición real. Como datos de salida adicionales, el algoritmo generará unos ficheros CSV con métricas que midan la precisión de la predicción mediante el cálculo de errores comparando la posición real del objeto y la predicha por el algoritmo.Computer vision techniques have evolved and gained relevance over the last years within the technological field, being implemented in a great variety of fields, being one of these and the main one in which this work will be mainly focused on, that of autonomous driving. This project will develop an algorithm capable of detecting and geolocating objects captured from different cameras located in a moving vehicle, making use of images taken from different perspectives, as well as different metadata that provide the necessary information to locate the objects within the route taken by the vehicle. To achieve these objectives, it will be necessary to analyze and filter a database that provides all the necessary information for the use of the algorithm, as well as the study and application of triangulation techniques through artificial vision to be used by the algorithm. In addition to this, it will be necessary to design and implement the algorithm that will make use of the filtered data and the triangulation techniques previously studied, having the final objective of measuring the effectiveness of its prediction and results with respect to the real position of the object. As a final result, an algorithm will be obtained which, based on specific information introduced by means of CSV format files, will carry out the process of geolocating the objects contained in the data, obtaining their position within the route taken by the vehicle. In addition to these coordinates, the algorithm will perform a graphical representation with an aerial view perspective of the scenario and the objects that compose it, representing the position of the vehicle at the moment in which it performed each of the detections of the object, as well as the position predicted by the algorithm of the object and its actual position. As additional output data, the algorithm will generate CSV files with metrics that measure the accuracy of the prediction by calculating errors comparing the actual position of the object and the one predicted by the algorithm.Grado en Ingeniería Informátic