Pothole detection in farmland roads with multi-scale edge enhancement and feature fusion

Detecting potholes on unstructured farm roads is a critical prerequisite for the autonomous navigation of agricultural machinery. Yet the challenge remains, due to complex field environments, blurred target boundaries, irregular morphologies, and significant scale variations. In this study, a pothole detection model, named POT-YOLOv11n, was proposed using the baseline YOLOv11n architecture. Three components were incorporated: A Multi-scale Edge Information Enhancement module was integrated multi-branch convolutional layers with channel-wise attention mechanisms to strengthen edge feature extraction and boundary discrimination for potholes with vague contours; A Pyramid Pooling and Large Kernel Attention Fusion module was combined large kernel separable attention with spatial pyramid pooling to enhance multi-scale contextual feature capture; And a Feature Screening and Contextual Anchor Attention mechanism was introduced in the neck network to optimize feature fusion and contextual modeling. Salient features were focused on to suppress the irrelevant information from complex backgrounds. In static evaluation, a pothole dataset of a farm road was constructed using diverse samples with varying scales, irregular shapes, and different lighting. The improved POT-YOLOv11n model achieved a mean Average Precision of 83.20%, indicating an improvement of 2.47 percentage points over the baseline YOLOv11n model. An F1-score of 80.30% was obtained to balance the precision and recall performance. Compact architecture was maintained with only 2.22 million parameters, with an inference speed of 248.83 frames per second, thereby meeting real-time requirements for autonomous navigation. In dynamic evaluation, field tests were conducted on a representative farm road segment with typical potholes. Practical applicability was assessed under realistic operation, where the agricultural vehicle was operated at three conventional working speeds, including low speed at 5 km/h, medium speed at 10 km/h, and high speed at 15 km/h. The better performance was achieved in the continuous image acquisition and online detection. As the vehicle speed increased, motion blur effects intensified, leading to a decline in detection accuracy. At a low speed of 5 km/h, the mean Average Precision of 80.8% was slightly lower than the static test with precision and recall of 82.1% and 79.6%, respectively. While the F1-score remained consistent with static testing, indicating effective transferability of detection during real-world deployment; At medium speed of 10 km/h, the mean Average Precision decreased to 79.8% with the precision and recall of 80.9% and 78.8%, respectively; At high speed of 15 km/h, the mean Average Precision further declined to 75.5% with the recall decreasing to 74.2%, indicating increased missed detections under severe motion blur, yet a mean Average Precision of 75.5% was obtained under such high-speed conditions. Therefore, the multi-scale edge enhancement module also provided for the compensation against motion blurs, preserving detection performance beyond what conventional architectures. The POT-YOLOv11n model achieved effective performance in detecting potholes on unstructured farm roads over different operating speeds. Boundary ambiguity, scale variation, and motion blur were solved using multi-scale edge enhancement, large kernel attention fusion, and feature screening mechanisms. While a lightweight architecture and high inference speed were suitable for practical deployment, the consistent performance in the static and dynamic tests, particularly the robust data under low and medium speeds, and the compensated performance at high speed for the practical reliability of the approach. Therefore, the POT-YOLOv11n model can provide reliable support for the autonomous navigation of agricultural machinery in complex field environments, thus contributing to smart agriculture and farming. A combination of accuracy, efficiency, and robustness was obtained to position motion blur. The finding can provide a promising solution for real-time obstacle detection in vision navigation systems in smart agriculture.