{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T14:11:03Z","timestamp":1792332663918,"version":"4.4.0"},"reference-count":72,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Container surface damage detection is critical for ensuring the structural integrity and operational safety of intermodal freight transport. However, visual pseudo-textures arising from rust stains, specular reflections, and paint weathering cause frequent false positives, while the scarcity of puncture-type defects (Hole class) leads to missed detections. Existing YOLO-family detectors address neither the frequency-domain characteristics of such pseudo-textures nor the physical priors inherent in genuine structural damage. In this paper, we propose PPFS-YOLO, a physics-prior frequency-spatial fusion framework built upon YOLOv12s. Two lightweight modules are introduced: (1) Frequency-Spatial Fusion (FSF), which applies a learnable spectral mask in the Fourier domain and performs gated fusion with spatial features to suppress pseudo-texture responses; and (2) Edge-Guided Auxiliary Supervision Module (FIM), which encodes Sobel-derived edge priors as a differentiable L1 constraint (Lphy) to regularize feature learning toward physically plausible damage boundaries. Three pairs of FSF\u2013FIM are inserted into the YOLOv12s neck and head at P3, P4, and P4-head scales. Experiments on a container damage dataset containing 7013 images and three classes (Dent, Hole, Rusty) demonstrate that PPFS-YOLO achieves 64.86% mAP@50, a +12.35 percentage-point improvement over the YOLO12s baseline (SGD, unified optimizer), with only +0.79 M additional parameters (+8.6%) and a modest latency overhead of 2.9 ms (17.2 ms vs. 14.3 ms at 640\u00d7640 on an NVIDIA RTX 3090 GPU (NVIDIA Corporation, Santa Clara, CA, USA)). Ablation analysis reveals that Lphy is the critical catalyst: without it, the combined FSF+FIM modules yield only +0.83 pp, whereas the full model achieves +12.10 pp\u2014underscoring the synergy between frequency-domain fusion and physics-prior regularization.<\/jats:p>","DOI":"10.3390\/s26103224","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T09:12:41Z","timestamp":1779268361000},"page":"3224","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PPFS-YOLO: Physics-Prior Frequency-Spatial Fusion for Robust Container Surface Damage Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8131-5907","authenticated-orcid":false,"given":"Jingze","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1825-328X","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"ref_1","unstructured":"United Nations Conference on Trade and Development (2024). 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