Machine Learning Model for Overfilled Container Identification
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Solution Overview
Problem
Current methods for identifying overfilled containers in refuse collection and other industries are inefficient, leading to operational issues and revenue loss due to manual processes and lack of scalability.
Innovation Solution
A machine learning model trained on images from refuse collection vehicles to predict whether a container is overfilled by preprocessing images and using deep learning techniques, allowing for automated identification and reduced computational latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification of overfilled containers is used, then operational issues and revenue loss occur, but automation complexity and implementation cost increase
Solution Approach 1:
The patent replaces manual visual inspection with an automated machine learning-based image analysis system. Cameras mounted on refuse collection vehicles capture images of containers, and a trained machine learning model automatically determines whether containers are overfilled, eliminating the need for manual assessment and significantly improving identification efficiency.
Solution Approach 2:
The system enables self-service identification where the machine learning model autonomously analyzes container images and generates overfill determinations without human intervention. The model processes images captured during normal collection operations and automatically identifies overfilled containers, allowing the system to serve itself in the identification task.
2Adaptability or versatility
If machine learning models are deployed across large fleets of vehicles, then scalability improves, but computational processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on extensive datasets of container images before deployment. The models are trained offline to recognize patterns of overfilled containers, so that during actual field operations, the pre-trained models can quickly process new images with minimal computational overhead, enabling scalability across large fleets without excessive processing demands.
Solution Approach 2:
The system segments the computational task by deploying independent machine learning models on individual vehicles or in distributed clusters. Each model processes images from its local camera system, dividing the overall computational load across multiple independent units rather than requiring centralized processing of all fleet data, thereby improving scalability while managing computational requirements.
3Measurement precision
If image preprocessing is performed to reduce bias, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent applies partial action by implementing selective image preprocessing steps that address the most significant sources of bias and variability in container images. Rather than applying all possible preprocessing operations, the system focuses on key transformations such as normalization, lighting adjustment, and geometric correction that have the greatest impact on identification accuracy, thereby maintaining precision while minimizing processing time.
Data Source
AI summary
Among other things, the techniques described herein include a method for receiving a plurality of images of one or more containers while the one or more containers are being emptied, the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images; and optimizing the machine learning model by performing learning against the validation set, the optimized machine learning model being used to generate a prediction for a new image of a container, the prediction indicating whether the container in the new image was overfilled prior to the new container being emptied.