EVIDENCE BASED VERIFICATION OF CAMERA LENS CLEANING OUTCOMES IN INTELLIGENT VISION SYSTEMS

Abstract

An automated camera cleaner can complete its commanded action while contamination still degrades the image. This paper proposes a procedure for verifying visual recovery after a cleaning intervention. The procedure associates observations with a specific intervention, excludes images captured before mechanical and optical settling, preserves comparable pre-cleaning evidence, and requires a bounded sequence of acceptable observations. Residual contamination, obstruction of designated critical regions, and an independently validated image-usability check jointly determine acceptance. An exact calculation with an assumed binary Markov error model examines the effect of temporally correlated false-pass decisions. For a marginal false-pass probability of 0.10, three consecutive passes within 30 observations give a false-clear probability of 2.506% under independence and 38.149% when lag-one correlation is 0.70. These are analytical scenario results, not measurements from a camera or cleaning device. The analysis shows why persistence alone cannot establish recovery and why the observation horizon must be specified. The contribution is an intervention-level verification procedure and its conditional reliability analysis; physical cleaning efficiency and real-world perception recovery remain to be experimentally evaluated.