A DETR-like detector-based semi-supervised object detection method for Brassica Chinensis growth monitoring

Abstract

Object detection technology plays a crucial role in crop growth monitoring within smart agriculture. However, data labeling is a costly process necessary for constructing a large-scale dataset, which is essential to prevent overfitting in deep learning-based object detection models. Semi-Supervised Object Detection (SSOD) presents a cost-effective solution to reduce labeling and model training expenses; nevertheless, existing SSOD algorithms fall short in addressing the specific challenges posed by detection tasks in Brassica Chinensis growth monitoring. Specifically, the two-stage object detector cannot be well-suited for scenes characterized by severe occlusion and complex backgrounds. The Non-Maximum Suppression (NMS) may filter out numerous true positives in scenarios with severe occlusion. Moreover, the label assignment enlarges the negative effects of the noise introduced by teacher model’s prediction, resulting in potential divergence. To tackle these challenges, we propose an end-to-end SSOD method based on Detection Transformer (DETR), which streamlines the post-processing without NMS and adopts a more advanced bipartite matching assignment strategy. These modifications tailor the semi-supervised training method to better align with the unique characteristics of detection tasks in Brassica Chinensis growth monitoring. Furthermore, two key techniques: low threshold filtering and decoupled optimization, are introduced to address class-imbalance and multi-task optimization conflict in the tasks, respectively. In the end, we conduct experiments using two self-constructed Brassica Chinensis image datasets to validate the effectiveness of the proposed method, which demonstrates state-of-the-art (SOTA) performance in both tasks. For plant detection, the proposed method achieves an mAP of 74.1 using only 5 % of the total data volume (18 images). In the wormhole detection task, the method achieves an AP50 of 73.7 using 5 % of the total data volume (73 images). These impressive results meet the requirements for practical applications in Brassica Chinensis growth monitoring.

Plant phenotyping relevance

Brassica chinensisの生育画像から植物および食害（wormhole）を検出する半教師あり画像解析法を開発・検証しており、植物状態の取得方法が研究の中心である。

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