Improved R-FCN for Banana Bunch Identification in Natural Environment Context
The detection and identification of banana bunches are crucial for intelligent fruit collection.However,due to the complex orchard landscape and the similarity of banana and leaf backgrounds,the automatic,fast and accurate identification of banana bunches become a challenge for intelligent fruit collection.Therefore,an improved R-FCN model is proposed to detect banana bunches in complex backgrounds.A large number of images are collected from the banana plantation base and enhanced by rotation and color conversion techniques.During the training,these enhanced images are used concomitantly with the original images.The tailored ResNet network and pixel-weighted loss function are used to improve the detection speed and segmentation effect.The test results show that the accuracy of this method at AP50 and AP70 reaches 93.1%and 90.4%,respectively,and the detection time is shortened to 0.187s,which is faster and more accurate than other image detection algorithms.
NETL
NSTL
万方数据
