Aircraft Critical Component Status Detection for Preflight Inspection via Unmanned Ground Vehicle Platform

Traditional preflight critical component inspections rely heavily on manual visual checks by maintenance crews, resulting in inefficient detection processes, significant safety risks, and high omission rates. The civil aviation industry urgently requires automated inspection solutions to enhance safety and efficiency. However, existing computer vision approaches are constrained by challenges such as complex lighting variations on aprons and significant scale variations of targets. To address these issues, this paper proposes an autonomous unmanned ground vehicle (UGV)-based detection framework for preflight critical component status detection, enhancing the intelligence of preflight inspections by using a UGV to support maintenance personnel in round-the-plane inspections. Specifically, we propose an aircraft critical component status detection method for preflight inspection via a UGV platform to accurately identify the states of nine key components (e.g., landing gear pins and electronic equipment bay doors) in apron scenarios. The luminance generation branch and multiscale detection branch are designed and introduced into the proposed algorithm to achieve state-of-the-art performance in preflight critical component status detection tasks. To establish an evaluation benchmark, we construct an Aircraft Pre-flight Inspection Safety-critical Detection dataset derived from line maintenance checklists, containing precise mask annotations of nine critical components captured via a UGV. Experimental results on our dataset demonstrate that the proposed method outperforms state-of-the-art object detection algorithms and meets preflight inspection requirements.