Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD

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.

[1]  Larry S. Davis,et al.  Learning From Noisy Anchors for One-Stage Object Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Xin Geng,et al.  Label Distribution Learning , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[3]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Radu Tudor Ionescu,et al.  Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction , 2018, 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).

[5]  Nabarun Bhattacharyya,et al.  Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks , 2008, IEEE Transactions on Instrumentation and Measurement.

[6]  A. K. Hazarika,et al.  Quality assessment of fresh tea leaves by estimating total polyphenols using near infrared spectroscopy , 2018, Journal of Food Science and Technology.

[7]  Xiongwei Wu,et al.  Recent Advances in Deep Learning for Object Detection , 2019, Neurocomputing.

[8]  Xin Geng,et al.  Theoretical Analysis of Label Distribution Learning , 2019, AAAI.

[9]  Bin Yang,et al.  Multi-Task Multi-Sensor Fusion for 3D Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Yi Liu,et al.  Salient Object Detection via Two-Stage Graphs , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Fuqiang Zhou,et al.  FSSD: Feature Fusion Single Shot Multibox Detector , 2017, ArXiv.

[13]  Xiaojin Gong,et al.  Adaptive Fusion for RGB-D Salient Object Detection , 2019, IEEE Access.

[14]  Long Chen,et al.  Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model , 2019, IEEE Access.

[15]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[16]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[18]  Dimitris N. Metaxas,et al.  ASSD: Attentive Single Shot Multibox Detector , 2019, Comput. Vis. Image Underst..

[19]  C. C. Wu,et al.  Developing Situation of Tea Harvesting Machines in Taiwan , 2015 .

[20]  Geng Ya.  Research on the Application of Automation Software Control System in Tea Garden Mechanical Picking , 2019 .

[21]  Hao Chen,et al.  FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[22]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[23]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Runu Banerjee Roy,et al.  Black tea classification employing feature fusion of E-Nose and E-Tongue responses , 2019, Journal of Food Engineering.

[25]  Wei Liu,et al.  DSSD : Deconvolutional Single Shot Detector , 2017, ArXiv.

[26]  Meng Joo Er,et al.  A local binary pattern based texture descriptors for classification of tea leaves , 2015, Neurocomputing.

[27]  Р Ю Чуйков,et al.  Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector , 2017 .

[28]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Yin Zhou,et al.  End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds , 2019, CoRL.

[30]  Qingming Huang,et al.  Corner Proposal Network for Anchor-free, Two-stage Object Detection , 2020, ECCV.

[31]  Bin Chen,et al.  Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning , 2020, IEICE Trans. Inf. Syst..

[32]  Tanzila Saba,et al.  Brain tumor detection using fusion of hand crafted and deep learning features , 2020, Cognitive Systems Research.

[33]  Christopher Zach,et al.  SPP-Net: Deep Absolute Pose Regression with Synthetic Views , 2017, ArXiv.