Frame-Level Accident Recognition via Detection Confidence Aggregation: A Cross-Domain Validation Framework for Thai Roadway Surveillance
| dc.contributor.author | Gabbualoy, Somprasonk | |
| dc.contributor.author | Phasukkit, Pattarapong | |
| dc.contributor.author | Houngkamhang, Nongluck | |
| dc.date.accessioned | 2026-08-06T10:55:57Z | |
| dc.date.available | 2026-08-06T10:55:57Z | |
| dc.date.issued | 2026-07-01 | |
| dc.description.abstract | Real-time roadway surveillance now leans hard on automated detection. How a model trained in one geographic context actually behaves on another, though, is still underexplored for Southeast Asian deployments. We answer that question for Thai roadway closed-circuit television with a cross-domain validation framework. A YOLOv11n (Ultralytics v8.2.0; Ultralytics, Los Angeles, CA, USA) detector trained with focal loss feeds a confidence-aggregation step that turns per-detection scores into a per-frame accident score, and we put four aggregation operators head-to-head. Reliability comes from DeLong variance estimation paired with non-parametric bootstrap on 1245 Thai frames that carry 23 positive accident events. Under maximum-class aggregation the proposed configuration reaches a frame-level AUROC of 0.959 ± 0.020 across three random seeds. Under top-K aggregation it reaches 0.965 ± 0.018. Per-seed DeLong 95 percent intervals exclude chance performance throughout. We also evaluate three baseline configurations: YOLOv5su comes in at 0.738, YOLOv8n at 0.868, and a Chiang Mai-tuned YOLOv11n variant at 0.918. The architectural progression seen on standard benchmarks therefore carries cleanly into the cross-domain setting. The same Chiang Mai-tuned variant reached an in-domain mAP50 of 0.952 yet only 0.918 cross-region AUROC on a separate Thai region, which is a quiet but clear signal that geographic proximity within a country does not on its own remove distributional shift. Bounding-box localisation appears as a secondary diagnostic because the operational target here is frame-level alerting rather than pixel-precise annotation. Edge deployment optimisation falls outside the present scope. What the work leaves behind is a reproducible baseline and a statistical protocol that follow-up Southeast Asian roadway-safety research can build on. | |
| dc.identifier.citation | Technologies, 14(7), 2026 | |
| dc.identifier.doi | 10.3390/technologies14070385 | |
| dc.identifier.issn | 22277080 | |
| dc.identifier.other | 2-s2.0-105045861410 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/18218 | |
| dc.source | Technologies | |
| dc.subject | accident recognition | |
| dc.subject | confidence aggregation | |
| dc.subject | cross-domain transfer | |
| dc.subject | object detection | |
| dc.subject | roadway surveillance | |
| dc.subject | statistical validation | |
| dc.subject | Thai traffic dataset | |
| dc.title | Frame-Level Accident Recognition via Detection Confidence Aggregation: A Cross-Domain Validation Framework for Thai Roadway Surveillance | |
| dc.type | Article |