Improving Out-of-Distribution Robustness for Wheat Head Detection: A Lightweight Modified YOLOv13 Approach

Wheat head detection is a critical component in high-throughput phenotyping, holding significant application value for wheat yield estimation and breeding analysis. With the continuous advancement of general object detection models, state-of-the-art detectors achieve high accuracy in same-distribution wheat head detection scenarios. However, when applied to cross-distribution environments, their performance often degrades significantly. To investigate this issue, this paper adopts the official division of the GWHD2021 public dataset as the cross-distribution evaluation setting. Using the recently state-of-the-art object detection model YOLOv13 as the baseline, we systematically explore effective approaches to enhance cross-distribution generalization performance in wheat head detection. Specifically, we propose two lightweight modifications to YOLOv13’s Full-PAD architecture and HyperACE’s core modules, analyzing their potential mechanisms: (1) Replacing scalar gating in Full-PAD with channel-level gating enables finer-grained branch injection control. Concurrently, channel-level gating introduces equivalent stronger weight penalties during training, generating additional regularization effects. Through exploratory controlled experiments aligning weight-penalty strengths, we find that this gain depends on both channel decoupling and the accompanying implicit regularization rather than on decoupling alone. (2) Removing Batch Normalization from HyperACE’s core C3AH modules and adopting normalization strategies independent of batch statistics—such as Identity, Group Normalization, or Instance Normalization. Preliminary experiments show that while this results in a small, directionally positive change, the change remains within run-to-run variance; we therefore do not claim BN removal as a reliable standalone improvement. Furthermore, combining channel gating with BN removal does not yield further additive improvements compared to channel gating alone, indicating an interaction effect. To address this, we analyze relevant statistics between channel gating and HyperACE branches, providing an exploratory explanation for this non-additive phenomenon. In summary, this paper delivers empirical evidence and preliminary insights for enhancing generalization performance in the cross-distribution wheat head detection task of the advanced general-purpose detection model YOLOv13.