Lin Ying, Zhang Jianjie, Jiang Zhangzhen, Tang Yiyu
College of Software, Xinjiang University, Urumqi 830091, China.
College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2023 Aug 8;23(16):7035. doi: 10.3390/s23167035.
The application of mulching film has significantly contributed to improving agricultural output and benefits, but residual film has caused severe impacts on agricultural production and the environment. In order to realize the accurate recycling of agricultural residual film, the detection of residual film is the first problem to be solved. The difference in color and texture between residual film and bare soil is not obvious, and residual film is of various sizes and morphologies. To solve these problems, the paper proposes a method for detecting residual film in agricultural fields that uses the attention mechanism. First, a two-stage pre-training approach with strengthened memory is proposed to enable the model to better understand the residual film features with limited data. Second, a multi-scale feature fusion module with adaptive weights is proposed to enhance the recognition of small targets of residual film by using attention. Finally, an inter-feature cross-attention mechanism that can realize full interaction between shallow and deep feature information to reduce the useless noise extracted from residual film images is designed. The experimental results on a self-made residual film dataset show that the improved model improves precision, recall, and mAP by 5.39%, 2.02%, and 3.95%, respectively, compared with the original model, and it also outperforms other recent detection models. The method provides strong technical support for accurately identifying farmland residual film and has the potential to be applied to mechanical equipment for the recycling of residual film.
地膜的应用对提高农业产量和效益做出了显著贡献，但残膜对农业生产和环境造成了严重影响。为了实现农业残膜的精准回收，残膜检测是首先要解决的问题。残膜与裸土之间的颜色和纹理差异不明显，且残膜具有各种尺寸和形态。为了解决这些问题，本文提出了一种利用注意力机制检测农田残膜的方法。首先，提出了一种具有强化记忆的两阶段预训练方法，使模型能够在有限的数据下更好地理解残膜特征。其次，提出了一种具有自适应权重的多尺度特征融合模块，通过注意力增强对残膜小目标的识别。最后，设计了一种能够实现浅层和深层特征信息充分交互以减少从残膜图像中提取的无用噪声的特征间交叉注意力机制。在自制的残膜数据集上的实验结果表明，改进后的模型与原模型相比，精度、召回率和平均精度均值分别提高了5.39%、2.02%和3.95%，并且优于其他近期的检测模型。该方法为准确识别农田残膜提供了有力的技术支持，具有应用于残膜回收机械设备的潜力。
