Congratulations to Shen Xinyu on his successful graduation!

Shen Xinyu graduated from Shanghai Normal University and began his master’s degree in the School of Computer Engineering and Science at Shanghai University in 2023. After joining the research group, he studied the intersection of computer vision and smart agriculture under the guidance of Professor Sun Yan, Professor Han Yuexing, and Professor Chen Qiaochuan, focusing on weed detection in complex farmlands. With their careful guidance, he completed the following research:

1. To address the weak feature representation ability and susceptibility to background interference of existing weed detection methods in complex farmlands, an FS-DETR dual-domain fusion Transformer detection model is proposed. Through joint modeling of spatial-domain and frequency-domain attention, the model effectively enhances fine-grained feature representation in farmland images, accurately distinguishes crops and weeds with similar appearances, and further combines a constraint-guided label assignment strategy to optimize sample matching, greatly improving detection accuracy and training stability in complex field scenes.

2. To address the single decoder modeling and unstable predictions in dense occlusion scenes of existing Transformer detection algorithms, an enhanced decoder version, FS-DETR-D, is proposed. The method introduces dynamic query assignment and hyperedge higher-order query interaction mechanisms to adaptively handle complex farmland scenes with varying sparsity and occlusion, and accurately models the spatial structure of dense weed targets. At the same time, an adaptive perceptual loss function is designed to dynamically adjust supervision strength, effectively solving the pain points of missed detections, false detections, and large prediction fluctuations in traditional methods, and significantly improving the stability and prediction consistency of the decoding process.

3. To address the poor scene adaptability and limited generalization ability of existing farmland visual detection algorithms, a complete weed detection system with dual-domain fusion and decoding enhancement is built. Extensive experiments on multiple public datasets such as WeedCrop, LincolnBeet, and MH-Weed16 demonstrate that this system achieves better accuracy and robustness than mainstream detection methods, and the decoding-enhanced model achieves stable accuracy gains over the baseline model, effectively adapting to various real-world complex farmland operations and improving smart agriculture vision detection solutions.

After graduation, Shen Xinyu joined Honor Device Co., Ltd. and will work in software development and technology research and development. During his postgraduate studies at Shanghai University, Shen Xinyu studied diligently, kept improving, and continuously enhanced his professional and research capabilities. He was fortunate to work alongside many excellent mentors and friends and gained a great deal along the way. We hope that in his future journey he will always keep his ideals in mind, stay grounded, fear no hardship, and move forward bravely.

Essay: Research on Complex Farmland Weed Detection Methods Based on Transformers

Code: https://github.com/han-yuexing/2026-thesis-sxy-code