关键词: 巢门区域, 蜜蜂数量, 深度学习, SSD

Abstract: Traditional bee monitoring mostly relies on labourer and experience, and the level of information is low, so automatic monitoring of bees has been concerned widely. Recently, object detection based on deep learning has developed rapidly and achieved good results in many fields. SSD model is a target detection model based on convolutional neural network, which has the advantages of speed and accuracy. The light of the beehive gate area is changeable, and the environment are complex. Bee colony also has complex situations such as mutual occlusion and shadow.  In this paper, SSD model was used to detect honeybees in the hive gate  and to make data statistics analysis. The results showed that the precision of the method selected was 96.34%, 92.52% and 88.06% in the case of small, general and large bees’ number, which was 11%, 19% and 25% higher than that of the traditional method, respectively. Furthermore,  SSD model had strong adaptability to different environment such as illumination, weather, shooting distance and could detect and deal with complex conditions such as bee shadow, blur, occlusion, etc. The detection results provided strong support for monitoring outside the hive, and also laid a foundation for statistics of in-and-out number based on bee tracking.

Key words: hive gate area, bee detection, deep learning, SSD