The Revolutionary Role of AI in Wildlife Conservation

Swift development of artificial intelligence has being pushing revolutions in all walks of life, and the function of data is crucial. In the training process of AI models, high-quality datasets are like fuel, directly determines the performance and accuracy of the algorithm. With demand soaring for intelligence, various datasets have gradually become core resources for research and application.

The task of refined urban governance aims to use artificial intelligence (AI) technology to perform intelligent image recognition on problematic events (such as road damage, garbage dumping, road occupation, etc.) provide technical support.

Data labeling method and AI algorithm for refined urban governance

· Data annotation is usually presented in the form of event label annotation + specific object rectangle frame annotation.

Relevant algorithms are essentially target detection + target classification tasks. This type of task is a basic task of artificial intelligence. It is usually developed based on deep learning and is identified by extracting specific target features and background features of different events. Commonly used target detection + target classification algorithms (such as yolo) frameworks and networks (such as resnet) can be used.

The difficulty of fine urban governance tasks mainly lies in the background complexity of real urban scenes and the complexity of specific goals in events.

Real city background complexity

The types, positions, and shapes of objects in images in different locations and different perspectives in the city are very different. For example, alleys, urban main roads, and street merchants all have completely different target distributions, which makes it difficult for the algorithm to extract effective features.

The specific target complexity involved in the event

The appearance of the main target is also very different under a specific event. For example, road damage may have different shapes and degrees of damage. Road operators may be trolleys, tricycles or motor vehicles. Advertisements may be banners, vertical plaques, garbage It may be oversized waste, kitchen waste, etc. The complexity of the specific target also puts forward higher requirements for the accuracy of the algorithm.

Nexdata has designed a series of training data systems to solve refined urban governance tasks. Through higher-quality finished data sets, it can solve the AI task requirements and difficulties in the governance process, and provide technical support for urban governance personnel to carry out their work, thereby improving Improve the level of urban governance and upgrade smart cities.

10 Categories – 8,085 Groups of Urban Refined Management Data

10 Categories – 8,085 Groups of Urban Refined Management Data. The collection scenes include street, snack street, shop entrance, corridor, community entrance, construction site, etc. The data diversity includes multiple scenes, different time periods(day, night), different photographic angles. The urban refined management categories in the images were annotated with rectangular bounding boxesThis data can be used for tasks such as urban refined management.

189 Videos-Electric Bicycle Entering Elevator Data

189 Videos-Electric Bicycle Entering Elevator Data，the total duration is 1 hour 58 minutes 40.72 seconds. The data covers different types of elevators, different types of electric bicycles, different time periods. The data can be used for tasks such as electric bicycle detection, electric bicycle recognition .

With the advancement of data technology, we are heading towards a more intelligent world. The diversity and high-quality annotation of datasets will continue to promote the development of AI system, create greater society benefits in the fields like healthcare, intelligent city, education, etc, and realize the in-depth integration of technology and human well-being.