Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning
In the field of robot target recognition research driven by artificial intelligence (AI), factors such as the disordered distribution of targets, the complexity of the environment, the massive scale of data, and noise interference have significantly restricted the improvement of target recognition accuracy. Against the backdrop of the continuous iteration and upgrading of current AI technologies, to meet the demand for accurate recognition of disordered targets by intelligent robots in complex and changeable scenarios, this study innovatively proposes an AI - based intelligent robot disordered target recognition method using reinforcement learning. This method processes the collected target images with the bilateral filtering algorithm, decomposing them into low - illumination images and reflection images. Subsequently, it adopts differentiated AI strategies, compressing the illumination images and enhancing the reflection images respectively, and then fuses the two parts of images to generate a new image. On this basis, this study deeply integrates deep learning, a core AI technology, with the reinforcement learning algorithm. The enhanced target images are input into a deep reinforcement learning model for training, ultimately enabling the AI - based intelligent robot to efficiently recognize disordered targets. Experimental results show that the proposed method can not only significantly improve the quality of target images but also enable the AI - based intelligent robot to complete the recognition task of disordered targets with higher efficiency and accuracy, demonstrating extremely high application value and broad development prospects in the field of AI robots.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement Learningreinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
The Responsibility Problem in Neural Networks with Unordered Targets
We discuss the discontinuities that arise when mapping unordered objects to neural network outputs of fixed permutation, referred to as the responsibility problem. Prior work has proved the existence of the issue by iden…
Fast Surrogate Learning for Multi-Objective UAV Placement in Motorway Intelligent Transportation System
We address multi-objective unmanned aerial vehicle (UAV) placement for motorway intelligent transportation systems, where deployments must balance coverage, link quality, and UAV count under geometric constraints. We con…
Uni-AIMS: AI-Powered Microscopy Image Analysis
This paper presents a systematic solution for the intelligent recognition and automatic analysis of microscopy images. We developed a data engine that generates high-quality annotated datasets through a combination of th…
Synthetic Data GenerationDeep set conditioned latent representations for action recognition
In recent years multi-label, multi-class video action recognition has gained significant popularity. While reasoning over temporally connected atomic actions is mundane for intelligent species, standard artificial neural…
Action RecognitionAtomic action recognitionComposite action recognitionTemporal Action LocalizationResearch on Mosaic Image Data Enhancement for Overlapping Ship Targets
The problem of overlapping occlusion in target recognition has been a difficult research problem, and the situation of mutual occlusion of ship targets in narrow waters still exists. In this paper, an improved mosaic dat…