Skip-YOLO: Domestic Garbage Detection Using Deep Learning Method in Complex Multi-scenesShow others and affiliations
2023 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, no 1, article id 139Article in journal (Refereed) Published
Abstract [en]
It is of great significance to identify all types of domestic garbage quickly and intelligently to improve people's quality of life. Based on the visual analysis of feature map changes in different neural networks, a Skip-YOLO model is proposed for real-life garbage detection, targeting the problem of recognizing garbage with similar features. First, the receptive field of the model is enlarged through the large-size convolution kernel which enhanced the shallow information of images. Second, the high-dimensional features of the garbage maps are extracted by dense convolutional blocks. The sensitivity of similar features in the same type of garbage increases by strengthening the sharing of shallow low semantics and deep high semantics information. Finally, multiscale high-dimensional feature maps are integrated and routed to the YOLO layer for predicting garbage type and location. The overall detection accuracy is increased by 22.5% and the average recall rate is increased by 18.6% comparing the experimental results with the YOLOv3 analysis. In qualitative comparison, it successfully detects domestic garbage in complex multi-scenes. In addition, this approach alleviates the overfitting problem of deep residual blocks. The application case of waste sorting production line is used to further highlight the model generalization performance of the method. © 2023, Springer Nature B.V.
Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023. Vol. 16, no 1, article id 139
Keywords [en]
Dense convolution block, Feature mappings, Garbage detection, Image procession, YOLOv3, Complex networks, Deep learning, Feature extraction, Image enhancement, Learning systems, Semantics, Domestic garbage, Feature map, Feature mapping, Higher dimensional features, Learning methods, Quality of life, Convolution
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:bth-25355DOI: 10.1007/s44196-023-00314-6ISI: 001057554400001Scopus ID: 2-s2.0-85168809613OAI: oai:DiVA.org:bth-25355DiVA, id: diva2:1795352
2023-09-082023-09-082023-09-15Bibliographically approved