Construction Waste Image Recognition on Vibrating Conveyor Belts

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Solution Overview

Problem

Existing construction waste sorting methods face issues of low recycling purity, inefficient manual operations, health hazards from high dust and noise, and difficulties in detection and recognition due to conveyor belt vibrations and image feature loss in dusty environments.

Innovation Solution

An improved YOLOv8 object detection model with receptive field channel attention convolution and multidimensional cooperative attention modules, combined with an enhanced SRGAN algorithm for preprocessing, is used to enhance feature extraction and recognition, incorporating lightweight modules and optimized loss functions for accurate and fast detection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional mechanical operations and manual sorting are used for construction waste, then the sorting process can be performed, but the recycling purity is low and manual operations are inefficient

Engineering Contradiction:

Improvesorting efficiencyVSAvoidrecycling purity

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces traditional mechanical operations and manual sorting with an intelligent vision-based detection system. The system uses image processing algorithms to automatically identify, classify, and guide the sorting of construction waste materials, eliminating the need for manual inspection and mechanical mixing processes while achieving both high efficiency and high purity recycling.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The detection system enables the sorting process to be self-directed through automatic material identification and classification. The system independently analyzes waste material images, determines material types, and generates sorting instructions without human intervention, allowing the sorting system to serve itself through intelligent automation.

Inventive Principle:

Principle #25Self-service

2Productivity

If construction waste is sorted on a vibrating conveyor belt, then continuous processing is achieved, but image features are lost due to vibration and occlusion

Engineering Contradiction:

Improvecontinuous processing capabilityVSAvoidimage feature loss

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent captures images of construction waste materials at optimal moments before significant vibration-induced distortion occurs. The system uses synchronized triggering and timing mechanisms to acquire images when materials are relatively stable on the conveyor belt, preserving critical visual features for accurate identification while maintaining continuous processing capability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces image processing algorithms as an intermediary between the vibrating conveyor belt and the detection system. These algorithms compensate for vibration-induced distortions, occlusions, and blurriness by enhancing image quality, reconstructing lost features, and extracting meaningful information even from degraded images, thereby bridging the gap between continuous processing and feature preservation.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Difficulty of detecting and measuring

If images are captured in dusty environments, then detection can proceed, but image features become blurred making recognition difficult

Engineering Contradiction:

Improvedetection capabilityVSAvoidrecognition accuracy

Core Design Contradiction:

Difficulty of detecting and measuringVSMeasurement precision

Solution Approach 1:

The patent employs advanced image processing algorithms as an intermediary to counteract the blurring effects of dusty environments. These algorithms filter out dust particles, enhance contrast, and restore obscured features in captured images, enabling accurate material recognition despite challenging atmospheric conditions while maintaining detection capability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent adjusts image processing parameters such as contrast enhancement, sharpness correction, and noise filtering to compensate for dust-induced blurriness. By dynamically modifying these parameters based on environmental conditions, the system maintains recognition accuracy even when images are degraded by dusty atmospheric interference.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250363780A1Method for intelligent sorting, detection, and recognition of construction waste

Publication Date: 2025.11.27 BEIJING UNIV OF CIVIL ENG & ARCHITECTURE

AI summary

A method for intelligent sorting, detection, and recognition of construction waste. Construction waste images are collected as the original image sample set in the construction waste sorting site, the SRGAN algorithm is improved to preprocess the construction waste dataset images, and the preprocessed dataset is labeled and divided into train, validation, and test sets at an 8:1:1 ratio. An improved YOLOv8 detection and recognition model which introduces receptive field attention convolutions and multidimensional collaborative attention modules in the feature extraction part of the backbone is applied. This method for intelligent sorting, detection, and recognition of construction waste replaces manual labor with the construction waste intelligent sorting, detection and recognition method, solves the problem of loss of construction waste image features due to vibration of the conveyor belt and mutual occlusion of construction waste during the intelligent sorting process.