ApproxDet: Machine Vision for Object Detection on Mobiles

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Researchers at Purdue University have developed an advanced video analytic system for content and contention-aware object detection that can be integrated via mobile devices, known as AppoxDet. ApproxDet has applications in solving problems pertaining to machine vision and object identification where compute resources are constrained. ApproxDet offers a multi-branch detection kernel that features a data-driven modeling approach based on real-time performance metrics along with a latency scheduler to allow changes to execution branches once objects are detected. Further, a content-aware feature extractor is integrated to determine the height and width of objects while a contention sensor determines resource levels and availability for user connectivity. Applications include autonomous driving, process or manufacturing automation, agriculture, and more.

Technology Validation: This technology has been validated by benchmarking the system against AdaScale and YOLOv3. ApproxDet offered 52% lower latency and 11.1% higher accuracy.

-More accurate than existing solutions
-Reduced latency through scheduling and changeable execution branches
-Ideal for mobile applications
-Adaptive user interface

-Autonomous driving, vehicle and traffic management
-Process/manufacturing automation
-Augmented Reality
Mar 31, 2022
Utility-Gov. Funding
United States

Mar 31, 2021
Provisional-Gov. Funding
United States
Purdue Office of Technology Commercialization
The Convergence Center
101 Foundry Drive, Suite 2500
West Lafayette, IN 47906

Phone: (765) 588-3475
Fax: (765) 463-3486