关键词: 深度学习, 目标检测, SSD模型, ResNet-101模型, 特征金字塔网络（FPN）

Abstract: In order to realize automatic fruit picking in natural environment, there are problems caused by environment, obstacles and other factors, resulting in low detection accuracy and generalization of target fruit and other practical problems. Taking apples, oranges and bananas as research objects, a Single Shot Detector（SSD） improved model ?is?proposed based on deep learning. The classical SSD adopts the multi-scale feature fusion method to extract different scale features from different layers of network for prediction, but does not use enough low-level features to make detection effect of small objects poor. VGG16?input?model?is?replaced?for?classical?SSD?training?with?ResNet?101, ?and?the?Feature?Pyramid?Network （FPN） structure?is?adopted?to?fuse?upper-level?features?with?low-level?features. Experiment results show that the average detection accuracy of the improved SSD300 and SSD512 fruit detection model is 83.05% and 84.24%. There is also a small increase after the data is enhanced, which is suitable for accurate detection of fruits in natural environments.

Key words: deep learning, target detection, Single Shot Detector（SSD） model, ResNet-101 model, ?Feature?Pyramid Network（FPN）