Adaptive Zoom Control Approach Of Load-View Crane Camera For Worker Detection

Abstract

Zoom Camera Is Essential For Detecting Objects From The Top-View. The Deep Learning Detection Algorithm Can Fail To Handle Scale Invariance, Especially For Detectors Whose Input Size Is Changed In An Extremely Wide Range. The Adaptive Zoom Feature Can Enhance The Quality Of The Deep Learning Worker Detection. In This Paper, We Introduce An Automatic Zoom Control Approach And Demonstrate Its Efficacy In Real-World Top-View Object Detection. To Avoid Further Data Gathering And Extensive Re-Training, The Zoom Adaptability Method Of The Load-View Crane Camera Is Able To Support The Deep Learning Algorithm, Specifically In The High Scale Variant Problem. The Finite State Machine Is Employed For Control Strategies To Adapt The Zoom Level To Cope Not Only With Inconsistent Detection But Also Abrupt Camera Movement During Lifting Operation. As The Result, The Detector Is Able To Detect A Small Size Object By Smooth Continuous Zoom Control Without Additional Training