Autonomous headland operations require reliable boundary perception and timely behavioral decisions in unstructured field environments. This study proposes a perception-and-decision framework for autonomous agricultural machinery. A CA-SP-DeepLabV3+ semantic segmentation model is developed by integrating Coordinate Attention, Strip Pooling, and a combined Boundary–Dice loss to enhance the continuity and localization of weak and elongated headland boundaries. To facilitate deployment on resource-constrained computing platforms, structured pruning and knowledge distillation are further applied to compress the segmentation network. A stereo-vision-based three-dimensional boundary reconstruction method is then used to estimate the vehicle-relative distance to the headland boundary. Based on the estimated distance, tractor–implement parameters, operating speed, and selected turn type, the decision module calculates the required headland width and distance thresholds for deceleration and turn preparation. Experimental results show that the compressed segmentation model achieved IoU values of 92.55% and 96.53% for the headland and field classes, respectively, while reducing the number of parameters and FLOPs by 28.48% and 48.43% compared with the uncompressed model. Across four representative headland environments and reference distances ranging from 10 to 30 m, the relative ranging errors ranged from 1.30% to 7.03%, and the total processing latency remained below 45.5 ms under the evaluated conditions. The decision experiments further showed that the proposed method generated headland-width plans and deceleration thresholds consistent with the geometric and kinematic constraints of different tractor–implement combinations and operating conditions. These results support the feasibility of the proposed framework for boundary perception, distance estimation, and behavioral decision support under the evaluated conditions.