Warehouse Storage-Location Monitoring With Object Category Detection
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Solution Overview
Problem
Laser sensors used in warehouse storage-location management cannot distinguish between different types of objects, leading to increased false detection events and low accuracy in identifying storage locations.
Innovation Solution
A method involving video data acquisition, category detection using a category detection model, and deep-learning image recognition to identify objects such as humans, vehicles, or goods, with detection results transmitted to a warehouse scheduling system for real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a laser sensor is used to monitor warehouse storage-locations, then the monitoring coverage is achieved, but the accuracy of storage-location identification deteriorates due to inability to distinguish object categories
Solution Approach 1:
A computer vision system serves as an intermediary between the camera and the storage-location monitoring function. The system processes images through object detection algorithms to identify and distinguish different object categories (forklifts, staff members, goods), thereby achieving accurate storage-location identification without directly replacing the camera hardware.
Solution Approach 2:
The patent replaces the traditional laser sensor-based mechanical detection system with a computer vision system that uses cameras and image processing algorithms. This substitution enables the system to not only detect the presence of objects but also to distinguish their categories, thereby improving measurement precision while maintaining monitoring coverage.
2Reliability
If a laser sensor is used for monitoring, then the system is simple, but false detection events increase due to inability to distinguish object categories
Solution Approach 1:
The computer vision system acts as an intermediary layer that processes camera images to distinguish between different object categories. This intermediary processing eliminates false detection events by accurately identifying whether detected objects are forklifts, staff members, or goods, thereby improving detection reliability.
Solution Approach 2:
The patent replaces the simple laser sensor system with a more complex computer vision system that uses image processing and category detection algorithms. This substitution significantly reduces false detection events by enabling the system to distinguish object categories, thereby improving reliability despite the increased system complexity.
3Measurement precision
If video data processing and category detection are implemented, then object distinction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing video data to extract key frames and detecting objects in advance. The category detection model is trained beforehand to recognize common object categories, enabling rapid identification during real-time monitoring without requiring extensive computational resources for each individual detection.
Data Source
AI summary
A method for warehouse storage-location monitoring is provided. The method includes: obtaining video data of a warehouse storage-location area, and obtaining a target image corresponding to the warehouse storage-location area based on the video data, detecting the target image based on a category detection model, to determine a category of each object appearing in the target image, obtaining a detection result by detecting a status of each object based on the category of each object, transmitting the detection result to a warehouse scheduling system, the detection result being used for the warehouse scheduling system to monitor the warehouse storage-location area.