Recent achievements of the team - A dual-domain detection Transformer framework for weed detection in complex agricultural scenes
Our team published the paper “A Dual-Domain Detection Transformer for Fine-Grained Weed Detection in Complex Agricultural Scenes” in Information Sciences (IF: 6.8, QSCI Zone 2 TOP). The School of Computer Engineering and Science at Shanghai University is the first institution listed.
Weed detection is a key technology in precision agriculture, intelligent weeding, and smart farmland management. However, in complex agricultural environments, existing detection methods are prone to false detections and missed detections due to factors such as the highly similar appearances of crops and weeds, severe object occlusion, complex background interference, and significant scale variations, making it difficult to meet practical application needs. To address these challenges, this paper proposes FS-DETR (Frequency-Spatial Detection Transformer), a dual-domain fusion detection Transformer framework that collaboratively models spatial-domain and frequency-domain information to achieve accurate fine-grained weed detection in complex agricultural scenes.
Specifically, this paper proposes a Hybrid Feature Fusion (HFF) module that integrates multi-scale spatial features with high-frequency information in the frequency domain, enhancing the representation of fine-grained texture features and edge information, thereby effectively alleviating detection difficulties caused by crop-weed overlap and complex background interference. Meanwhile, a Dual Domain Attention Mechanism (DDAM) is designed to adaptively fuse frequency-domain attention with deformable attention, fully exploiting spatial structural information and frequency-domain texture information during the encoding stage to improve feature extraction and target discrimination in complex agricultural environments. Furthermore, a Gaussian Distribution-based and Constraint-guided Label Assignment (GCLA) module is constructed to optimize the label matching process for weed and crop targets, improving the quality of supervision and detection accuracy during training.
Experimental results on three public agricultural weed datasets, WeedCrop, LincolnBeet, and MH-Weed16, show that FS-DETR achieves excellent performance. Specifically, FS-DETR obtains AP scores of 47.2%, 60.4%, and 32.5% on WeedCrop, LincolnBeet, and MH-Weed16, respectively, improving upon the baseline model by 1.4%, 1.0%, and 0.6%. In addition, for small-object weed detection tasks, FS-DETR improves over the current second-best methods by 1.2% and 0.2%, demonstrating strong fine-grained object detection capability and robustness in complex scenes, and providing a new technical solution for precision weed management in intelligent agriculture.
Essay: A Dual-Domain Detection Transformer for Fine-Grained Weed Detection in Complex Agricultural Scenes
Code: https://github.com/YanSun-github/FS-DETR
