Pavement surface texture significantly affects its skid resistance. To characterize pavement surface texture and analyze its correlation with skid resistance, this paper proposes a novel three-dimensional (3D) texture evaluation index: mean texture surface area density (MTSAD). First, field tests were conducted on Chengdu Greenway pavement using a portable laser scanner to collect high-precision texture data, while a pendulum friction tester was employed to measure the British Pendulum Number (BPN). Subsequently, digital image processing technology was employed for the 3D reconstruction of pavement texture. Leveraging the high-resolution data characteristics and incorporating the concept of infinite subdivision, an innovative method for calculating the pavement texture surface area was developed, ultimately yielding the MTSAD. Finally, polynomial regression analysis was performed to examine the correlation between MTSAD and BPN, revealing a coefficient of determination (R²) of 0.8302. The results demonstrate a close relationship between MTSAD and pavement friction, while proving that texture indices that are easy to promote can be obtained through high-precision 3D point cloud images, and validating the potential of non-contact texture measurement as a viable alternative to conventional contact-based friction testing methods.

Field-road cracks in high-standard farmland are often slender, low-contrast, and surrounded by complex textures that cause U-Net-based models to misclassify aggregates, tire marks, shadows, and repair edges as cracks. To reduce these false positives, this study develops a task-oriented context-guided hard-negative background suppression network (CGHN-Net) based on Squeeze-and-Excitation U-Net (SE-U-Net). The context-guided skip gate (CGSG), a same-resolution adaptation of additive attention gating, uses already upsampled decoder features as semantic guides to filter encoder skip features at all three scales. Hard-negative background suppression loss (HNBS Loss), a background-restricted hard-example mining objective, further targets elevated-probability responses within ground-truth background regions. The dataset comprised 2235 vehicle-acquired grayscale pavement images from independent sessions and mutually exclusive road sections: 1684 for training, 464 for validation, and 87 for testing. Across three random seeds, CGHN-Net achieved Dice, IoU, precision, recall, and FP area ratio values of 0.8682 ± 0.0055, 0.7791 ± 0.0102, 0.8780 ± 0.0161, 0.8734 ± 0.0266, and 0.0042 ± 0.0008, respectively. Against U-Net, Attention U-Net, UNet++, DeepLabV3+, SegFormer-B0, and BGCrack, it achieved the highest Dice, IoU, and recall, indicating the strongest overall overlap and crack recovery. Sequence-aware paired analysis against UNet++ preserved contiguous acquisition order through block lengths of 3, 5, and 10 images, and all block-bootstrap confidence intervals excluded zero. On 100 held-out crack-free images, CGHN-Net also achieved the lowest post-processed image-level false-alarm rate and FP area ratio among the included models. Additional three-seed validation on the independently acquired public CrackForest Dataset (CFD), with the selected models retrained on mutually exclusive CFD partitions, showed that CGHN-Net achieved Dice, IoU, and recall of 0.6679 ± 0.0162, 0.5028 ± 0.0182, and 0.9486 ± 0.0085, respectively, exceeding UNet++ and BGCrack in overlap and crack recovery. The results support the task-oriented combination of same-resolution skip filtering and background-restricted hard-example mining for suppressing texture-induced false responses, while the CFD experiment is interpreted as independent public-dataset retraining rather than zero-shot transfer.

Field roads in high-standard farmland are continuously exposed to agricultural machinery loads, environmental degradation, and limited maintenance conditions. Consequently, pavement cracks in these environments are often characterized by slender morphology, low contrast, irregular boundaries, and highly complex background textures. Although existing U-Net-based crack segmentation models enable pixel-level detection, they are prone to misclassifying crack-like background patterns as genuine cracks in scenes containing coarse aggregates, tire marks, shadows, repair edges, and strong pavement noise, thereby increasing false-positive predictions. To address this issue, this study proposes a Context-Guided Hard-Negative Background Suppression Network (CGHN-Net). Built upon a Squeeze-and-Excitation Channel Attention-U-Net (SE-U-Net)backbone, CGHN-Net introduces a Context-Guided Skip Gate (CGSG) between the encoder and decoder. The CGSG exploits high-level semantic information from the decoding stage to spatially filter low-level skip features, thereby suppressing interference from complex pavement textures during feature fusion. In addition, a Hard-Negative Background Suppression Loss (HNBS Loss) is designed to select hard negative samples with relatively high predicted crack probabilities and large losses from ground-truth background regions and impose additional constraints on these samples, further reducing high-confidence spurious crack responses. The experimental dataset was constructed from two-dimensional grayscale pavement images acquired by a multifunctional pavement inspection vehicle and contained 2,235 samples, including 1,684 training images, 464 validation images, and 87 test images. Compared with representative semantic segmentation models, including U-Net, Attention U-Net, UNet++, and DeepLabV3+, CGHN-Net achieved a Dice coefficient of 0.8682 ± 0.0055, an Intersection over Union (IoU) of 0.7791 ± 0.0102, a precision of 0.8780 ± 0.0161, and a recall of 0.8734 ± 0.0266 over three random-seed trials, while reducing the mean false-positive area ratio (FP Area Ratio) to 0.0042 ± 0.0008. Ablation experiments demonstrate that CGSG effectively improves the discrimination of crack-related regions, whereas HNBS Loss further suppresses spurious crack predictions in complex-textured backgrounds when used jointly with CGSG.