{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T12:56:15Z","timestamp":1777380975473,"version":"3.51.4"},"reference-count":14,"publisher":"SAGE Publications","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIS"],"published-print":{"date-parts":[[2023,10,24]]},"abstract":"<jats:p>The manual sorting of recyclable garbage has caused several issues such as the wastage of human resources and low resource utilization. To solve this problem, an improved Single Shot Multibox Detector (SSD) deep learning approach has been developed for recyclable garbage detection. To reduce the number of parameters and make the model easier to deploy and apply, a lightweight network called RepVGG has been chosen to replace the VGG16 network in the SSD. Additionally, the auxiliary convolutional layer structure of the SSD has been modified to further reduce the number of parameters. Additionally, the SK module has been integrated to adaptively adjust the size of the receptive field and enhance the detection accuracy. Experimental results of Waste Classification data set from Kaggle website have demonstrated that the improved SSD model has better detection accuracy and real-time performance, with an accuracy of 95.23%, which is 4.33 percentage points higher than the original SSD, and a detection speed of up to 64 FPS. This algorithm can be better applied in industry.<\/jats:p>","DOI":"10.3233\/ais-230124","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T11:31:57Z","timestamp":1698147117000},"page":"1-14","source":"Crossref","is-referenced-by-count":1,"title":["Improving resource recycling based on deep learning"],"prefix":"10.1177","author":[{"given":"Yunjian","family":"Xu","sequence":"first","affiliation":[{"name":"School of Intelligent Engineering, Guangdong AIB Polytechnic, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiyin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Internet of Things Application Technology, Guangdong AIB Polytechnic, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AIS-230124_ref2","doi-asserted-by":"crossref","unstructured":"W.\u00a0Cui, W.\u00a0Zhang, J.\u00a0Green et al., Yolov3-darknet with adaptive clustering anchor box for garbage detection in intelligent sanitation, in: Proceedings of the 2019 3rd International Conference on Electronic Information Technology and Computer Engineering, 2019.","DOI":"10.1109\/EITCE47263.2019.9095167"},{"key":"10.3233\/AIS-230124_ref3","doi-asserted-by":"crossref","unstructured":"X.\u00a0Ding, Y.\u00a0Guo, G.\u00a0Ding et al., ACNet: Strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks, in: Proceedings of the IEEE\/CVF international Conference on Computer Vision, 2019, pp.\u00a01911\u20131920.","DOI":"10.1109\/ICCV.2019.00200"},{"key":"10.3233\/AIS-230124_ref5","doi-asserted-by":"crossref","unstructured":"R.\u00a0Girshick, Faster R-CNN, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp.\u00a01440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"10.3233\/AIS-230124_ref6","doi-asserted-by":"crossref","unstructured":"R.\u00a0Girshick, J.\u00a0Donahue, T.\u00a0Darrell et al., Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp.\u00a0580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"10.3233\/AIS-230124_ref7","doi-asserted-by":"publisher","first-page":"104","DOI":"10.3969\/j.issn.1673-291X.2020.34.041","article-title":"Problems and countermeasures in the implementation of \u201cRegulations of Shanghai municipality on domestic wastemanagement\u201d","volume":"34","author":"Gong","year":"2020","journal-title":"Economic Research Guide"},{"issue":"9","key":"10.3233\/AIS-230124_ref8","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Transactions On Pattern Analysis And Machine Intelligence"},{"key":"10.3233\/AIS-230124_ref10","unstructured":"H.\u00a0Jie, S.\u00a0Li and S.\u00a0Gang, Squeeze-and-excitation networks, in: Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (Cvpr), 2018, pp.\u00a07132\u20137141."},{"key":"10.3233\/AIS-230124_ref12","doi-asserted-by":"crossref","unstructured":"X.\u00a0Li, W.\u00a0Wang, X.\u00a0Hu et al., Selective kernel networks, in: Proceedings of the IEEE\/CVF Conference on Computervision and Pattern Recognition, 2019, pp.\u00a0510\u2013519.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"10.3233\/AIS-230124_ref15","first-page":"21","volume-title":"Ssd: Single Shotmultibox Detector. 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