Обнаружение объектов на изображениях с большим разрешением на основе их пирамидально-блочной обработки

In the paper the algorithm for object detection in high resolution images is proposed. The approach uses multiscale image representation followed by block processing with the overlapping value. For each block the object detection with convolutional neural network was performed. Number of pyramid layers is limited by the Convolutional Neural Network layer size and input image resolution. Overlapping blocks splitting to improve the classification and detection accuracy is performed on each layer of pyramid except the highest one. Detected areas are merged into one if they have high overlapping value and the same class. Experimental results for the algorithm are presented in the paper.Предлагается алгоритм для обнаружения объектов на изображениях с большим разрешением, основанный на многомасштабном представлении изображения, пирамидально-блочной обработке с перекрытием, применении сверточной нейронной сети для каждого блока и объединении обнаруженных областей. Количество слоев пирамиды определяется размерами изображения и входного слоя используемой сверточной нейронной сети. На всех уровнях, кроме самого верхнего, выполняется блочное разбиение, а применение при этом перекрытия позволяет улучшить правильную классификацию объектов, которые разделяются на фрагменты и расположены в соседних блоках. Решение об объединении таких областей принимается на основе анализа метрики пересечения над объединением для них и принадлежности к одному классу. Представленные результаты тестирования алгоритма подтверждают, что рассмотренный подход позволяет повысить точность обнаружения объектов небольших размеров на изображениях с большим разрешением

Object Detection in Video Surveillance Based on Multiscale Frame Representation and Block Processing by a Convolutional Neural Network

A method for detecting objects in high-resolution images is proposed that is based on representing an image as a set of its copies of decreasing scale, splitting it into blocks with overlap at each level of the image pyramid except for the top one, detecting objects in the blocks, and analyzing objects at the boundaries of adjacent blocks to merge them. The number of pyramid layers is determined by the size of the image and the input layer of the convolutional neural network (CNN). At all levels except for the top one, a block splitting is performed, and the use of overlap allows one to improve the correct classification of objects, which are divided into fragments and located in adjacent blocks. The decision to merge such fragments is made based on the analysis of the metric of intersection over union and membership in the same class. The proposed approach is evaluated for 4K and 8K images. To carry out experiments, a database is prepared with objects of two classes, person and vehicle, marked in such images. Networks of the You Only Look Once (YOLO) family of the third and fourth versions are used as CNNs. A quantitative assessment of the detection efficiency of objects is performed using the mAP metric for various combinations of parameters such as the degree of threshold confidence of the CNN and the percentage of intersection of blocks in the hierarchical representation of images. The results of the investigations are presented

Object detection algorithm for high resolution images based on convolutional neural network and multiscale processing

In this article we propose an effective algorithm for small object detection in high resolution images. We look at the image at different scales and use block processing by convolutional neural network. Pyramid layers number is defined by input image resolution and convolutional layer size. On each layer of pyramid except the highest we perform splitting overlapping blocks to improve small object detection accuracy. Detected areas are merged into one if they belong to the same class and have high overlapping value. In the paper experimental results using YOLOv4 for 4K and 8K images are presented. Our algorithm shows better detecting small objects results in high-definition video than YOLOv4

Applicability of Multispectral Imagery for Detection of Prescribed Fires and Rekindling

Forest fires are an increasingly relevant problem nowadays with the worsening of global warming’s most severe consequences. These fire occurrences, that can cause immense damage to forest ecosystems and have a great negative impact in peoples lives,begin often with rekindles. These problems can be very difficult to tackle, needing to involve a lot of people to surveil the areas at risk. A system capable of executing this surveillance protocol and alerting the fire fighting authorities of fire and possible rekindle occurrences would be extremely beneficial in these scenarios.A system aiming to achieve this goal is being implemented, composed of an UAV equipped with a multispectral camera, capturing aerial images of these areas. This dissertation presents a fire detection model to be used in prescribed fires and rekindling situations, identifying fire instances within the captured images. It makes use of the camera’s various spectral bands to highlight the areas at greatest risk and of deep learning technology to autonomously recognise these areas.Incêndios florestais são um problema cada vez mais relevante nos dias de hoje com o agravamento das consequências mais graves do aquecimento global. Estas ocorrências,que podem causar imensos danos aos ecossistemas florestais e ter um grande impacto negativo na vida das pessoas, são muitas vezes iniciadas por reacendimentos. Estes problemas podem ser muito difíceis de combater, necessitando de envolver muitas pessoas para vigiar as áreas de risco. Um sistema capaz de executar este protocolo de vigilância e alertar as autoridades de combate a incêndio sobre fogos e possíveis reacendimentos seria extremamente benéfico nestes cenários.Para alcançar este objetivo, está a ser implementado um sistema composto por um UAV, equipado com uma câmera multiespectral, que irá capturar imagens aéreas dessas áreas. Esta dissertação apresenta um modelo de detecção de incêndios para ser utilizado em situações de fogos controlados e reacendimentos, identificando ocorrências de fogo nas imagens capturadas. Faz uso das várias bandas espectrais da câmera para destacar as áreas de maior risco e de tecnologia de aprendizagem automática para reconhecer essas áreas de forma autônoma