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- Detecting and monitoring the development stages of wild flowers and plants using computer vision: approaches, challenges and opportunitiesPublication . Videira, João; Gaspar, Pedro Dinis; Soares, V.N.G.J.; Caldeira, J.M.L.P.Wild flowers and plants play an important role in protecting biodiversity and providing various ecosystem services. However, some of them are endangered or threatened and are entitled to preservation and protection. This study represents a first step to develop a computer vision system and a supporting mobile app for detecting and monitoring the development stages of wild flowers and plants, aiming to contribute to their preservation. It first introduces the related concepts. Then, surveys related work and categorizes existing solutions presenting their key features, strengths, and limitations. The most promising solutions and techniques are identified. Insights on open issues and research directions in the topic are also provided. This paper paves the way to a wider adoption of recent results in computer vision techniques in this field and for the proposal of a mobile application that uses YOLO convolutional neural networks to detect the stages of development of wild flowers and plants.
- A mobile application for detecting and monitoring the development stages of wild flowers and plantsPublication . Videira, João; Gaspar, Pedro Dinis; Soares, V.N.G.J.; Caldeira, J.M.L.P.Wild flowers and plants appear spontaneously. They form the ecological basis on which life depends. They play a fundamental role in the regeneration of natural life and the balance of ecological systems. However, this irreplaceable natural heritage is at risk of being lost due to human activity and climate change. The work presented in this paper contributes to the conservation effort. It is based on a previous study by the same authors, which identified computer vision as a suitable technological platform for detecting and monitoring the development stages of wild flowers and plants. It describes the process of developing a mobile application that uses YOLOv4 and YOLOv4-tiny convolutional neural networks to detect the stages of development of wild flowers and plants. This application could be used by visitors in a nature park to provide information and raise awareness about the wild flowers and plants they find along the roads and trails.
- Detecting wear and tear in pedestrian crossings using computer vision techniques: approaches, challenges, and opportunitiesPublication . Rosa, Gonçalo J.M.; Afonso, João M.S.; Gaspar, Pedro Dinis; Soares, V.N.G.J.; Caldeira, J.M.L.P.Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings.
- A new approach towards waste container detection in smart citiesPublication . Valente, Miguel; Caldeira, J.M.L.P.; Soares, V.N.G.J.; Silva, Hélio; Gaspar, Pedro DinisThis paper presents a new approach to help redesigning waste management for the cities of the future. The current state of tracking waste containers is rigid, inefficient and hard to oversee. Although attempts have been made in the past using radio-frequency identification for waste container detection, it has shown problems like flexibility, cost and environmental impact. We propose and demonstrate a solution based on the use of computer vision techniques, for object detection and classification, towards the differentiation between different types of waste containers.