A Reliable Defect Confirmation Method for Drainage Pipeline Inspection Based on Vision–LiDAR–Ultrasonic Fusion

Drainage pipeline environments are typically characterized by darkness, high humidity, water accumulation, sediment deposition, reflective surfaces, and severe occlusions. These challenging conditions make conventional single-sensor inspection methods highly susceptible to environmental interference, resulting in false detections, missed defects, and insufficient reliability in defect confirmation. To address these challenges, this paper proposes a vision–LiDAR–ultrasonic multi-sensor fusion method for defect confirmation in drainage pipeline inspection. The three sensing streams are processed in parallel rather than using visual detection as the exclusive trigger: the vision branch performs high-recall screening of apparent defects, the LiDAR branch continuously evaluates geometric anomalies in spatially indexed point-cloud segments, and the ultrasonic branch independently evaluates wall-thickness and echo anomalies along the valid probe-contact path. Candidate regions proposed by any branch are merged through timestamp-, odometry-, and coverage-aware spatial association, after which all available visual, geometric, and acoustic evidence at each union candidate is mapped to basic probability assignments and fused using reliability-constrained Dempster–Shafer evidence theory. The five-run evaluation on the fixed 105-group test subset (18 defects and 87 non-defects) gives the proposed method an Accuracy of 97.7 ± 0.5%, Precision of 93.4 ± 2.2%, Recall of 93.3 ± 2.5%, F1-score of 93.3 ± 1.5%, and false-alarm rate of 1.4 ± 0.5%. Under the same test protocol, the vision-only baseline gives an F1-score of 81.1 ± 2.4% and a false-alarm rate of 3.9 ± 0.6%. These results are calculated from the measured per-group predictions obtained in the experiments.