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

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.