摘要：

为应对复杂农田环境下残膜回收机拾取地膜时回收效率低和现有深度学习模型识别残膜时精准度低等问题, 基于YOLOv5s提出一种农田残膜检测模型YOLO-SDI。首先, 将空间金字塔池化(SPP)结构与高效层聚合网络(ELAN)注意力机制相结合, 以更好地聚焦关键局部特征, 提升小目标识别率; 其次, 使用DySample模块替代UpSample模块, 增强小目标的特征信息, 提高模型识别准确性; 随后, 引入InceptionNeXt模块, 通过并行卷积层捕捉不同尺度信息, 增强模型对全局特征的关注度, 从而提高检测鲁棒性; 最后, 采用软非极大值抑制(Soft-NMS)替代非极大值抑制(NMS)方法, 通过逐步衰减重叠框的置信度, 以更精细地调整目标框的位置和置信度, 提高锚框的定位精度。实验结果表明, 相较YOLOv5s模型, YOLO-SDI在精确率、召回率、F1值和均值平均精度(mAP)上分别提高了1.2、0.2、0.6和7.2百分点。该研究表明, YOLO-SDI模型在农业残膜管理和田间清洁度评价等实际应用中具有一定的潜力, 能够为提高农田残膜回收率提供有力的技术支撑。

关键词: 残膜检测, YOLOv5, 高效层聚合网络, InceptionNeXt, DySample, 软非极大值抑制

Abstract:

In complex farmland environments, machines used for recycling residual films may pick up plastic films, leading to low recovery efficiency. Existing deep learning models show low accuracy in identifying residual films. To address these issue, this paper proposes a farmland residual film detection model based on YOLOv5s, called YOLO-SDI. First, the Spatial Pyramid Pooling (SPP) structure is combined with the Efficient Layer Aggregation Network (ELAN) attention mechanism to better focus on key local features and improve the recognition rate of small targets. Next, the UpSample module is replaced with the DySample module, which enhances the feature information of small targets and improves the accuracy of model recognition. Subsequently, the InceptionNeXt module is introduced to capture information at different scales through parallel convolutional layers. This enhances the model's attention to global features, thus improving detection robustness. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used instead of Non-Maximum Suppression (NMS) to gradually attenuate the confidence of overlapping boxes; this allows to more finely adjust the position and confidence of the target box and improve the positioning accuracy of the anchor box. The experimental results show that, compared with the YOLOv5s model, YOLO-SDI improves the precision, recall, F1 value, and mean Average Precision (mAP) by 1.2, 0.2, 0.6, and 7.2 percentage points, respectively. The findings of this study indicate that the YOLO-SDI model has potential for practical applications such as agricultural residue management and field cleanliness evaluation and can provide strong technical support for improving the recovery rate of agricultural residues.

Key words: residual film detection, YOLOv5, Efficient Layer Aggregation Network (ELAN), InceptionNeXt, DySample, Soft Non-Maximum Suppression(Soft-NMS)