Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning

2025-03-10

AI Summary

To address the challenges of unordered object detection in complex scenes—particularly under noise, illumination variations, and image quality degradation—this paper proposes a novel framework integrating bilateral filtering-based image decomposition with deep reinforcement learning. The method innovatively couples illumination-reflection component decoupling, driven by bilateral filtering, with a Deep Q-Network (DQN) for joint optimization. It introduces a coordinated strategy of illumination compression and reflectance enhancement to improve model robustness and generalization to low-quality inputs. Experimental results demonstrate significant improvements: image quality is substantially enhanced; object detection accuracy increases by 23.6%; inference speed accelerates by 31.4%; and detection accuracy remains stable above 92.7% in dynamic, cluttered environments. This approach provides a scalable, real-time solution for accurate object recognition under unordered layouts and low signal-to-noise ratio conditions.

Technology Category

• Computer Vision: Object Detection & Categorization
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Improve robot target recognition accuracy in disordered environments
• Address noise and data complexity in AI-driven recognition systems
• Enhance efficiency and precision in recognizing unordered targets

Innovation

• Bilateral filtering for image decomposition
• Differentiated AI strategies for image enhancement
• Deep reinforcement learning for target recognition