Authors :
Chhavi Rajput
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/4yuzsy4w
Scribd :
https://tinyurl.com/yeymc2y3
DOI :
https://doi.org/10.5281/zenodo.14613871
Abstract :
When an customer places an order online,
they expect a fast and accurate delivery. Customer
demand for a seamless experience from placing an order
to receiving an undamaged order in the hands. To
provide this seamless experience to our customers, large
level of industrialization is happening on the backend
from picking each product, scanning the barcode and
putting the order on the conveyor belt after packaging
and shipping the order at the right address. However,
automation comes with certain risks of mis-sortation of
packages, damage defects during packaging the product,
barcode sticker alignment and the received product can
be hampered due to liquid spillage, open damage box,
uncovered tape and other factors.
Therefore, this research is an effort to identify the
damages and defective products before delivering the
order. With the help of computer vision technology,
cameras are placed on the top of each conveyor belt and
camera will share the images at every 3-5 seconds and
advance algorithm will be used to identify the defects or
damage packages. This paper will cover the computer
vision algorithm along with image processing
normalization techniques to identifying the damages due
to human interaction and leading late deliveries and
poor customer experience.
Keywords :
Image Processing, Computer Vision, Defect Detection.
References :
- Zhonghe Ren1 · Fengzhou Fang1,2 · Ning Yan1 · You Wu1 . State of the Art in Defect Detection Based on Machine Vision
- Senay Cakir, Marcel Gauß, Kai Häppeler, Yassine Ounajjar, Fabian Heinle, Reiner Marchthaler Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability
- Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei - ImageNet Large Scale Visual Recognition Challenge
- Òscar Lorente, Ian Riera, Aditya Rana - Image Classification with Classic and Deep Learning Techniques
- Suorong Yang, Weikang Xiao, Mengchen Zhang, Suhan Guo, Jian Zhao, Furao Shen - Image Data Augmentation for Deep Learning: A Survey
When an customer places an order online,
they expect a fast and accurate delivery. Customer
demand for a seamless experience from placing an order
to receiving an undamaged order in the hands. To
provide this seamless experience to our customers, large
level of industrialization is happening on the backend
from picking each product, scanning the barcode and
putting the order on the conveyor belt after packaging
and shipping the order at the right address. However,
automation comes with certain risks of mis-sortation of
packages, damage defects during packaging the product,
barcode sticker alignment and the received product can
be hampered due to liquid spillage, open damage box,
uncovered tape and other factors.
Therefore, this research is an effort to identify the
damages and defective products before delivering the
order. With the help of computer vision technology,
cameras are placed on the top of each conveyor belt and
camera will share the images at every 3-5 seconds and
advance algorithm will be used to identify the defects or
damage packages. This paper will cover the computer
vision algorithm along with image processing
normalization techniques to identifying the damages due
to human interaction and leading late deliveries and
poor customer experience.
Keywords :
Image Processing, Computer Vision, Defect Detection.