Unified Multi-Object Detection Model to Reduce Resource Load
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Solution Overview
Problem
Existing object detection models in computer vision are inefficient and resource-intensive as they require separate training and maintenance for each type of object of interest, hindering scalability and accuracy in retail environments.
Innovation Solution
A unified multi-object detection model using a deep learning convolutional neural network (CNN) that can identify multiple object types, such as pallets, pallet tags, and steel bars, within a single model, reducing the need for multiple models and improving accuracy through a labeled image overlay with color-coded indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate object detection models are trained for each object type, then detection accuracy for specific object types is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent applies a single multi-object detection model that can detect multiple different object types (pallets, pallet tags, steel bars, etc.) rather than requiring separate specialized models for each object type. This universal model reduces the overall system complexity while maintaining the ability to accurately detect various objects through a unified architecture trained on diverse object categories.
2Reliability
If multiple separate object detection models are maintained, then specialized detection capability is improved, but scalability and cost effectiveness deteriorate
Solution Approach 1:
The system employs a single versatile detection model that can handle multiple object types across different retail environments. This unified model is trained to recognize various objects including pallets, pallet tags, and steel bars, enabling the system to scale effectively as new object types are needed without requiring training and deployment of separate specialized models for each object category.
3Adaptability or versatility
If multiple object detection models are used, then comprehensive object coverage is improved, but processor load and memory consumption increase
Solution Approach 1:
The patent merges multiple separate detection models into a single integrated multi-object detection model. This unified model processes all object types through one computational architecture, reducing the total processor load and memory consumption compared to running multiple separate models simultaneously, while maintaining comprehensive coverage of different object types including pallets, tags, and structural elements.
Data Source
AI summary
Examples provide a multi-object detection model for identifying different types of objects of interest using input images of a selected area including a plurality of objects. A multi-object detection manager identifies instances of each object type by adding indicators for identifying the different types of objects, such as, but not limited to, pallets, pallet tags, horizontal bars, vertical bars, wooden bases on pallets, and empty spaces. The indicators are provided within an overlay superimposed on the image. The indicators include text-based labels and/or non-text based color-coded indicators, such as color-coded bounding boxes. In such cases, instances of a first type of object are identified using a first indicator and instances of a second type of object are identified using a different second indicator. The labeled image including the indicators is generated to enable users to determine the locations of objects within a retail environment with greater accuracy and efficiency.