RETINANET-BASED DIRECTED TARGET DETECTION FOR RECYCLABLE WASTE

ZHANG Zheng; QIU Dahe; JING Zibo; XUE Bo; HU Xinyu

doi:10.13205/j.hjgc.202406019

Volume 42 Issue 6

Jun. 2024

Turn off MathJax

Article Contents

Article Navigation > ENVIRONMENTAL ENGINEERING > 2024 > 42(6): 160-168

ZHANG Zheng, QIU Dahe, JING Zibo, XUE Bo, HU Xinyu. RETINANET-BASED DIRECTED TARGET DETECTION FOR RECYCLABLE WASTE[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 160-168. doi: 10.13205/j.hjgc.202406019

Citation:

ZHANG Zheng, QIU Dahe, JING Zibo, XUE Bo, HU Xinyu. RETINANET-BASED DIRECTED TARGET DETECTION FOR RECYCLABLE WASTE[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 160-168. doi: 10.13205/j.hjgc.202406019

ZHANG Zheng, QIU Dahe, JING Zibo, XUE Bo, HU Xinyu. RETINANET-BASED DIRECTED TARGET DETECTION FOR RECYCLABLE WASTE[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 160-168. doi: 10.13205/j.hjgc.202406019

Citation:

PDF( 11503 KB)

RETINANET-BASED DIRECTED TARGET DETECTION FOR RECYCLABLE WASTE

doi: 10.13205/j.hjgc.202406019

School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China

Received Date: 2023-06-24
Available Online: 2024-07-11

Abstract

Abstract

To realize the automation of waste sorting, the research of vision-based automatic detection of recyclable waste is of great importance. To realize the automation of waste sorting, the traditional horizontal frame target detection algorithm loses the directional information of the target during the detection, and the overlap of the positioning frame is serious so that the true length and width of the target cannot be obtained, which is unfavorable to the subsequent sorting. The algorithm is based on the improvement of the RetinaNet network, adding the angle prediction module in the detection head, using the PSC angle encoder to improve the angle return boundary problem, introducing the Balanced L1 loss function to balance the gradient contribution of simple and difficult samples, and replacing the backbone network with the Swin Transformer to enhance the feature extraction capability of the network. The network with angle prediction can locate the garbage more accurately, and the improved network accuracy (mAP) reaches 78.4%, which is 12 percentage points higher than the original algorithm, while the detection effect of PSC is better than other methods compared with other angle encoders.
- directed target detection,
- deep learning,
- waste detection,
- angle encoder

FullText(HTML)

References(20)

References

[1]	ZHU C X,QIAN J C,WANG B R. YOLOX on embedded device with CCTV & TensorRT for intelligent multicategories garbage identification and classification[J]. IEEE Sensors Journal,2022,22(16):16522-16532.
[2]	CAI X,SHUANG F,SUN X,et al. Towards lightweight neural networks for garbage object detection[J]. Sensors,2022,22(19):7455.
[3]	韦波,张衡,王斐,等.基于Faster R-CNN的海面垃圾检测研究[J].环境工程,2022,40(7):153-158.
[4]	赵珊,刘子路,郑爱玲,等.基于MobileNetV2和IFPN改进的SSD垃圾实时分类检测方法[J].计算机应用,2022,42(增刊1):106-111.
[5]	MA J,SHAO W,YE H,et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE Transactions on Multimedia,2018,20(11):3111-3122.
[6]	YANG X,YAN J,FENG Z,et al. R3det:refined single-stage detector with feature refinement for rotating object[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021,35(4):3163-3171.
[7]	DING J,XUE N,LONG Y,et al. Learning RoI transformer for oriented object detection in aerial images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:2849-2858.
[8]	XU Y,FU M,WANG Q,et al. Gliding vertex on the horizontal bounding box for multi-oriented object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,43(4):1452-1459.
[9]	YANG X,YAN J. Arbitrary-oriented object detection with circular smooth label[C]//Computer Vision-ECCV 2020:16th European Conference,Glasgow,UK,August 23-28,2020,Proceedings,Part Ⅷ 16. Springer International Publishing,2020:677-694.
[10]	YANG X,YANG X,YANG J,et al. Learning high-precision bounding box for rotated object detection via kullback-leibler divergence[J]. Advances in Neural Information Processing Systems,2021,34:18381-18394.
[11]	YU Y,DA F. Phase-shifting coder:Predicting accurate orientation in oriented object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023:13354-13363.
[12]	LIN T Y,GOYAL P,GIRSHICK R,et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017:2980-2988.
[13]	LIU Z,LIN Y,CAO Y,et al. Swin Transformer:hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021:10012-10022.
[14]	PANG J,CHEN K,SHI J,et al. Libra r-cnn:towards balanced learning for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:821-830.
[15]	LIN T Y,DOLLAR P,GIRSHICK R,et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:2117-2125.
[16]	DOSOVITSKIY A,BEYER L,KOLESNIKOV A,et al. An image is worth 16x16 words:Transformers for image recognition at scale[J]. ArXiv Preprint ArXiv:2010.11929,2020.
[17]	LIN T Y,GOYAL P,GIRSHICK R,et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017:2980-2988.
[18]	YUDIN D,ZAKHARENKO N,SMETANIN A,et al. Hierarchical waste detection with weakly supervised segmentation in images from recycling plants[J]. Available at SSRN 4183424.
[19]	RUSSAKOVSKY O,DENG J,SU H,et al. Imagenet large scale visual recognition challenge[J]. International Journal of Computer Vision,2015,115(3):211-252.
[20]	XIA G S,BAI X,DING J,et al. DOTA:a large-scale dataset for object detection in aerial images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:3974-3983.