Sustainable highway infrastructure maintenance critically depends on precise lane line detection, yet conventional inspection approaches remain resource-depleting, carbon-intensive, and hazardous to personnel. To mitigate these constraints and address the low accuracy and high parameterization of existing models, this study utilizes unmanned aerial vehicle (UAV) imagery and proposes a UAV-based highway lane line segmentation method using an improved DeepLabV3+ model that resolves multi-scale lane line segmentation challenges in UAV imagery. MobileNetV2 is used as the backbone network to significantly reduce the number of model parameters. The Squeeze-and-Excitation (SE) attention mechanism is integrated to enhance feature extraction capabilities, particularly at lane line edges. A Feature Pyramid Network (FPN) is incorporated to improve multi-scale lane line feature extraction. We introduce a novel Waterfall Atrous Spatial Pyramid Pooling (WASPP) module, utilizing cascaded atrous convolutions with strategic dilation rate adjustments to progressively expand the receptive field and aggregate contextual information across scales. The improved model outperforms the original DeepLabV3+ by 5.04% mIoU (85.30% vs. 80.26%) and 3.35% F1-Score (91.74% vs. 88.39%) while cutting parameters by 85% (8.03 M vs. 54.8 M) and reducing training time by 2 h 50 min, thereby enhancing the model’s accuracy in lane line segmentation, reducing the number of parameters, and lowering the carbon footprint.

Unmanned aerial vehicle (UAV) imagery offers a promising alternative to manual and vehicle-based inspection for highway pavement distress detection, but the high-angle perspective reduces the relative size and feature richness of small distresses and amplifies aliasing during downsampling, limiting the accuracy of existing detectors. To address these problems, this paper proposes an improved YOLOv8 algorithm with four coordinated modifications: (i) a Feature-Focusing Diffusion Pyramid Network (FFDPN) that replaces the conventional PAN to strengthen multi-scale feature fusion and preserve fine-grained details; (ii) an Information Interaction Detection Head (IIDH) that replaces the decoupled dual-branch head, sharing interaction features between the classification and regression branches via deformable convolution (DCNv2) to reduce parameters while improving task synergy; (iii) an Edge Information Extraction Module (EIEM) placed at the front of the backbone, which uses Sobel-based gradient response plus max-pooling to inject low-level edge priors; and (iv) a WaveletPool downsampling operator that decomposes features into LL/LH/HL/HH sub-bands to suppress aliasing of small-scale distresses. Experiments on 3408 UAV images of four distress categories (transverse, longitudinal, and alligator cracks and potholes) show that the improved model reaches 93.7% Precision, 89.6% Recall, and 96.0% mAP@0.50—a 12.2 percentage-point gain over YOLOv8n—while using only 2.41 × 10⁶ parameters and outperforming Faster R-CNN, DETR, YOLOv7-tiny, YOLOv9, YOLOv10n, YOLOv11n, and YOLO-World on the same benchmark. The model eliminates the duplicate and missed detections observed in baselines, at a moderate cost in FPS (30.3 vs. 57.1 for YOLOv8n).