Intelligent Detection of Parcels Based on Improved Faster R-CNN
Source abstract
Abstract
Parcel detection is crucial to achieving automatic sorting in intelligent logistics systems. Most parcels in logistics centers are currently detected manually, imposing low efficiency and high error rate, severely limiting logistics transportation efficiency. Therefore, there is an urgent need for automated parcel detection. However, parcels in logistics centers have challenges such as dense stacking, occlusion and background interference, making it difficult for existing methods to detect parcels accurately. To address the above problem, we developed an improved Faster R-CNN-based parcel detection model spurred by current deep-learning-based object detection trends. The proposed method first solves the false detection problem due to parcel mutual occlusion by augmenting Faster R-CNN with an edge detection branch and adding object edge loss to the loss function. Furthermore, the self-attention ROI Align module is proposed to address the problem of feature misalignment caused by the quantization rounding operation in the ROI Pooling module. The module uses an attention mechanism to filter and enhance the features and uses bilinear interpolation to calculate the feature pixel values, improving detection accuracy. The implementation of the proposed method was validated using parcel images collected in the field and the public dataset SKU110K and compared with four existing parcel detection methods. The results show that our method’s Recall, Precision, map@0.5 and Fps are 96.89%, 98.76%, 98.42% and 22.83%, respectively, which significantly improves the parcel detection accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle License Plate Recognition · Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Engineering Laboratory for Robot Visual Perception & Control Technology, Hunan University, Changsha 410082, China
School of Electrical and Information Engineering, Hunan University, Changsha 410114, China
College of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410000, China
Funding
National Natural Science Foundation of China
References (35)
Show source-provided references
1. Xia, K., and Weng, Z. (2016, January 19–21). Workpieces sorting system based on industrial robot of machine vision. Proceedings of the 2016 3rd International Conference on Systems and Informatics (ICSAI), Shanghai, China.10.1109/ICSAI.2016.7810992 ↗
2. Object detection with deep learning: A review10.1109/TNNLS.2018.2876865 ↗
3. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
4. Spatial pyramid pooling in deep convolutional networks for visual recognition10.1109/TPAMI.2015.2389824 ↗
5. Girshick, R. (2015, January 7–13). Fast r-cnn. Proceedings of the IEEE international conference on computer vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
6. Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–12). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada.
7. Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5–10). R-fcn: Object detection via region-based fully convolutional networks. Proceedings of the Neural Information Processing Systems, Barcelona, Spain.
8. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
9. Singh, B., Li, H., Sharma, A., and Davis, L.S. (2018, January 18–23). R-fcn-3000 at 30fps: Decoupling detection and classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00119 ↗
10. Cai, Z., and Vasconcelos, N. (2018, January 18–23). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00644 ↗
11. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
12. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.10.1007/978-3-319-46448-0_2 ↗
13. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
14. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
15. Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.324 ↗
16. Yoo, D., Park, S., Lee, J.Y., Paek, A.S., and So Kweon, I. (2015, January 7–13). Attentionnet: Aggregating weak directions for accurate object detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.305 ↗
17. Caicedo, J.C., and Lazebnik, S. (2015, January 7–13). Active object localization with deep reinforcement learning. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.286 ↗
18. Hierarchical object detection with deep reinforcement learning
19. Evolution of deep belief neural network parameters for robot object recognition and grasping10.1016/j.procs.2017.01.195 ↗
20. Kumra, S., and Kanan, C. (2017, January 24–28). Robotic grasp detection using deep convolutional neural networks. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.10.1109/IROS.2017.8202237 ↗
21. Robot grasp detection using multimodal deep convolutional neural networks10.1177/1687814016668077 ↗
22. Robotic grasping in multi-object stacking scenes based on visual reasoning10.1360/N092018-00169 ↗
23. Zeng, A., Song, S., Yu, K.T., Donlon, E., Hogan, F.R., Bauza, M., Ma, D., Taylor, O., Liu, M., and Romo, E. (2018, January 21–25). Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.10.1109/ICRA.2018.8461044 ↗
24. Robotic Sorting Method in Complex Scene Based on Deep Neural Network
25. Nguyen, A., Kanoulas, D., Caldwell, D.G., and Tsagarakis, N.G. (2017, January 24–28). Object-based affordances detection with convolutional neural networks and dense conditional random fields. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.10.1109/IROS.2017.8206484 ↗
26. Viereck, U., Pas, A., Saenko, K., and Platt, R. (2017, January 11–12). Learning a visuomotor controller for real world robotic grasping using simulated depth images. Proceedings of the Conference on Robot Learning, Brussels, Belgium.
27. Gao, W., Zhang, X., Yang, L., and Liu, H. (2010, January 9–11). An improved Sobel edge detection. Proceedings of the 2010 3rd International Conference on Computer Science and Information Technology, Chengdu, China.
28. The pascal visual object classes challenge 2012 (voc2012) development kit
29. Xie, S., and Tu, Z. (2015, January 7–13). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.164 ↗
30. Yu, Z., Feng, C., Liu, M.Y., and Ramalingam, S. (2017, January 21–26). Casenet: Deep category-aware semantic edge detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.191 ↗
31. Zhao, H., Jia, J., and Koltun, V. (2020, January 14–19). Exploring self-attention for image recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01009 ↗
32. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
33. Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., and Hassner, T. (2019, January 15–20). Precise detection in densely packed scenes. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00537 ↗
34. LabelMe: A database and web-based tool for image annotation10.1007/s11263-007-0090-8 ↗
35. Zou, Z., Shi, Z., Guo, Y., and Ye, J. (2019). Object detection in 20 years: A survey. arXiv.