Abstract
The accuracy of Fresh Tea Sprouts Detection (FTSD) is not high enough, which has become a big bottleneck in the field of vision-based automatic tea picking technology. In order to improve the detection performance, we rethink the process of FTSD. Meanwhile, motivated by the multispectral image processing, we find that more input information can lead to a better detection result. With this in mind, a novel Fresh Tea Sprouts Detection method via Image Enhancement and Fusion Single-Shot Detector (FTSD-IEFSSD) is proposed in this paper. Firstly, we obtain an enhanced image via RGB-channel-transform-based image enhancement algorithm, which uses the original fresh tea sprouts color image as the input. The enhanced image can provide more input information, where the contrast in the fresh tea sprouts area is increased and the background area is decreased. Then, the enhanced image and color image is used in the detection subnetwork with the backbone of ResNet50 separately. We also use the multilayer semantic fusion and scores fusion to further improve the detection accuracy. The strategy of tea sprouts shape-based default boxes is also included during the training. The experimental results show that the proposed method has a better performance on FTSD than the state-of-the-art methods.
Highlights
Automatic tea picking by machine is an effective way to solve the tea picking labor problem
We propose a novel Fresh Tea Sprouts Detection method via Image Enhancement and Fusion SSD (FTSDIEFSSD). e main contributions of our paper are as follows: (1) We propose a novel improvement strategy for object detection using input information increase via image enhancement. e added input information obtained by the image enhancement usually contains more salient features, which help to improve the accuracy of object detection
Implementation Details. e experiments are run with 4 NVIDIA Titan X GPUs, the FTSD-IEFSSD model is trained on TensorFlow, and other steps are implemented using MATLAB. e input image resolution is 400 × 400 with top view. e dataset contains 6000 images, which were acquired in Hangzhou, China, from March 20 to April 4, 2019
Summary
Automatic tea picking by machine is an effective way to solve the tea picking labor problem. The existing visionbased tea picking robots cannot meet the requirement of a high-quality tea picking task due to the poor fresh tea sprouts detection and the uncontrollable imaging condition [1,2,3]. E one-stage object detector is usually modeled as a simple regression problem and encapsulated all the computation in a single feed-forward CNN [11], which can effectively increase the detection speed. In the fresh tea sprouts detection task, the tea sprouts are much smaller than the ripe leaves, and it is hard to detect the tea sprouts quickly and efficiently via the existing methods. The requirement of detection speed and accuracy is high when the object detector is used in the vision-based tea picking robots
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
