Abstract:

In order to effectively detect the pests in granary and reduce the loss of grain, this paper presents a method of pest detection for grain silos based on SSD. Multiple convolution feature maps are used to detect pests in this method. The training speed and detection efficiency of SSD are improved by lightweight model structure and loss function optimization. In this experiment, 6 kinds of high-outbreak granary pest images are trained and tested, and the results show that this method has a higher mAP in the detection of granary pests compared with the current mainstream object detection method.

Key words: granary pest, loss of grain, object detection, Single Shot MultiBox Detector（SSD）, mAP

摘要：

为了对粮仓害虫进行有效地检测，减少粮食损失，提出一种基于SSD的粮仓害虫检测方法。该方法利用多个尺度的卷积特征图来检测害虫。通过轻量化模型结构和优化损失函数来提高SSD的训练速度和检测效率。实验将6类高爆发的粮仓害虫图像进行训练和测试，结果表明：该方法相比较于当前主流的目标检测方法在对粮仓害虫检测中具有更高的mAP。

关键词: 粮仓害虫, 粮食损失, 目标检测, SSD, mAP