背景:稻田杂草物体检测可提供有关杂草种类和位置的关键信息,以进行精确的喷洒,在实际农业生产中具有重要意义。然而,面对复杂多变的现实农场环境,传统的目标检测方法仍然难以识别小尺寸,闭塞和密集分布的杂草实例。为了解决这些问题,本文提出了一种多尺度特征增强的DETR网络,名为RMS-DETR。通过在DETR之上添加多尺度特征提取分支,该模型充分利用了不同语义特征层的信息,提高了现实场景下稻田杂草的识别能力。
方法:在DETR模型的基础上引入多尺度特征层,我们对不同的语义特征层进行了差异化设计。高层语义特征层采用Transformer结构,提取谷草和水稻之间的上下文信息。低级语义特征层利用CNN结构提取谷仓草的局部细节特征。引入多尺度特征层不可避免地导致模型计算量的增加,从而降低模型推理速度。因此,我们使用一种新型的Pconv(部分卷积)来代替模型中的传统标准卷积。
结果:与原始DETR模型相比,我们提出的RMS-DETR模型在我们构建的稻田杂草数据集和DOTA公共数据集上实现了3.6%和4.4%的平均识别精度提高,分别。平均识别精度分别达到0.792和0.851。RMS-DETR模型大小为40.8M,推理时间为0.0081s。与三个经典DETR模型(可变形DETR,锚点DETR和DAB-DETR),RMS-DETR模型的平均精度分别提高了2.1%,4.9%和2.4%。
结论:该模型能够在复杂的现实环境中准确识别稻田杂草,从而为精密喷涂和可变速率喷涂系统的管理提供了关键的技术支持。
BACKGROUND: Rice field weed object detection can provide key information on weed species and locations for precise spraying, which is of great significance in actual agricultural production. However, facing the complex and changing real farm environments, traditional object detection methods still have difficulties in identifying small-sized, occluded and densely distributed weed instances. To address these problems, this paper proposes a multi-scale feature enhanced DETR network, named RMS-DETR. By adding multi-scale feature extraction branches on top of DETR, this model fully utilizes the information from different semantic feature layers to improve recognition capability for rice field weeds in real-world scenarios.
METHODS: Introducing multi-scale feature layers on the basis of the DETR model, we conduct a differentiated design for different semantic feature layers. The high-level semantic feature layer adopts Transformer structure to extract contextual information between barnyard grass and rice plants. The low-level semantic feature layer uses CNN structure to extract local detail features of barnyard grass. Introducing multi-scale feature layers inevitably leads to increased model computation, thus lowering model inference speed. Therefore, we employ a new type of Pconv (Partial convolution) to replace traditional standard convolutions in the model.
RESULTS: Compared to the original DETR model, our proposed RMS-DETR model achieved an average recognition accuracy improvement of 3.6% and 4.4% on our constructed rice field weeds dataset and the DOTA public dataset, respectively. The average recognition accuracies reached 0.792 and 0.851, respectively. The RMS-DETR model size is 40.8 M with inference time of 0.0081 s. Compared with three classical DETR models (Deformable DETR, Anchor DETR and DAB-DETR), the RMS-DETR model respectively improved average precision by 2.1%, 4.9% and 2.4%.
CONCLUSIONS: This model is capable of accurately identifying rice field weeds in complex real-world scenarios, thus providing key technical support for precision spraying and management of variable-rate spraying systems.