The customer wants the AI engine to take the images of the pallets during various stages of the inbound, put-away and outbound processes, and detect the labels of interest, decode the values of the labels (bar and QR codes) and provides it to the inventory management system.
SCAN – LESS PALLET
SORTING
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Creating the dataset from scratch, instructed and trained the client team on-ground staff and SPOC to take videos and images of the pallets :
- in various warehouses and in various sections of the warehouse
- with a variety of image capture devices
- different types of pallets and different labels used for pallets
- different stages of the inbound, put-away and outbound process
- Created a custom detection and classification model for the client using open source object detection models employing Transfer learning and Retraining, to detect a pallet, labels of interest seen on the pallet, and identify the label and classify the label type
- Extraction of the labels of interest from the image and sent it to the barcode and QR code decoder
- Get the decoded values of the bar and QR codes and sent it to the Warehouse inventory management system
- When storing the pallets in the warehouse, the rack level bar codes also need to be scanned and the decoded values need to be sent to the Warehouse inventory management system, during the storage process
CHALLENGES
- Understand the different processes and identify the exact time to capture images
- Pallets will be covered with plastic wraps since they are stored in cold storage warehouses
- The storage spaces will be multi level deep, and will have low lighting conditions
TECHNOLOGIES / TOOLS
- Keras, TensorFlow , OpenCV
- ResNet50 used as feature pyramid network
- PyZBar
MODEL HIGHLIGHTS
Detector and classification model for the parts of the pallet
- Full pallet
- Box portion
- Pallet base
- Type of the pallet based on the classification of the base of pallet portion