{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:52:20Z","timestamp":1770742340860,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T00:00:00Z","timestamp":1689206400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T00:00:00Z","timestamp":1689206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J05236"],"award-info":[{"award-number":["2020J05236"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00371-023-02982-z","type":"journal-article","created":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T16:02:28Z","timestamp":1689264148000},"page":"2751-2759","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Lightweight deep learning model for logistics parcel detection"],"prefix":"10.1007","volume":"40","author":[{"given":"Guowei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wuzhi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xincheng","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,13]]},"reference":[{"issue":"7","key":"2982_CR1","doi-asserted-by":"publisher","first-page":"7791","DOI":"10.1007\/s11227-020-03558-7","volume":"77","author":"C-L Chen","year":"2021","unstructured":"Chen, C.-L., Deng, Y.-Y., Weng, W., Zhou, M., Sun, H.: A blockchain-based intelligent anti-switch package in tracing logistics system. J. Supercomput. 77(7), 7791\u20137832 (2021). https:\/\/doi.org\/10.1007\/s11227-020-03558-7","journal-title":"J. Supercomput."},{"key":"2982_CR2","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"4","key":"2982_CR3","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s00371-020-01831-7","volume":"37","author":"W Chen","year":"2021","unstructured":"Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: Yolo-face: a real-time face detector. Vis. Comput. 37(4), 805\u2013813 (2021). https:\/\/doi.org\/10.1007\/s00371-020-01831-7","journal-title":"Vis. Comput."},{"key":"2982_CR4","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"2982_CR5","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"2982_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2982_CR7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2982_CR8","doi-asserted-by":"publisher","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A. C.: SSD: single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2982_CR9","doi-asserted-by":"publisher","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. https:\/\/doi.org\/10.48550\/arXiv.2004.10934(2020)","DOI":"10.48550\/arXiv.2004.10934"},{"issue":"14","key":"2982_CR10","doi-asserted-by":"publisher","first-page":"7255","DOI":"10.3390\/app12147255","volume":"12","author":"H-K Jung","year":"2022","unstructured":"Jung, H.-K., Choi, G.-S.: Improved yolov5: efficient object detection using drone images under various conditions. Appl. Sci. 12(14), 7255 (2022). https:\/\/doi.org\/10.3390\/app12147255","journal-title":"Appl. Sci."},{"key":"2982_CR11","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y. M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696. https:\/\/doi.org\/10.48550\/arXiv.2207.02696(2022)","DOI":"10.48550\/arXiv.2207.02696"},{"key":"2982_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"2982_CR13","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: Shufflenet v2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116\u2013131 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"2982_CR14","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2982_CR15","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et\u00a0al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"2982_CR16","doi-asserted-by":"publisher","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430. https:\/\/doi.org\/10.48550\/arXiv.2107.08430 (2021)","DOI":"10.48550\/arXiv.2107.08430"},{"key":"2982_CR17","doi-asserted-by":"publisher","unstructured":"Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289. https:\/\/doi.org\/10.48550\/arXiv.1511.07289(2015)","DOI":"10.48550\/arXiv.1511.07289"},{"key":"2982_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","volume":"107","author":"S Elfwing","year":"2018","unstructured":"Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3\u201311 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2017.12.012","journal-title":"Neural Netw."},{"issue":"2","key":"2982_CR19","doi-asserted-by":"publisher","first-page":"365","DOI":"10.3390\/agronomy12020365","volume":"12","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Wu, R., Lin, Y., Li, C., Chen, S., Yuan, Z., Chen, S., Zou, X.: Plant disease recognition model based on improved yolov5. Agronomy 12(2), 365 (2022). https:\/\/doi.org\/10.3390\/agronomy12020365","journal-title":"Agronomy"},{"key":"2982_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2982_CR21","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 658\u2013666 (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"key":"2982_CR22","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"Y-F Zhang","year":"2022","unstructured":"Zhang, Y.-F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506, 146\u2013157 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2022.07.042","journal-title":"Neurocomputing"},{"issue":"3","key":"2982_CR23","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.3390\/s22031215","volume":"22","author":"X Xu","year":"2022","unstructured":"Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X., Yang, H.: Crack detection and comparison study based on faster R-CNN and mask R-CNN. Sensors 22(3), 1215 (2022). https:\/\/doi.org\/10.3390\/s22031215","journal-title":"Sensors"},{"issue":"13","key":"2982_CR24","doi-asserted-by":"publisher","first-page":"7803","DOI":"10.1007\/s00521-020-05521-2","volume":"33","author":"D Saavedra","year":"2021","unstructured":"Saavedra, D., Banerjee, S., Mery, D.: Detection of threat objects in baggage inspection with x-ray images using deep learning. Neural Comput. Appl. 33(13), 7803\u20137819 (2021). https:\/\/doi.org\/10.1007\/s00521-020-05521-2","journal-title":"Neural Comput. Appl."},{"key":"2982_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07106-8","author":"J Xue","year":"2022","unstructured":"Xue, J., Zheng, Y., Dong-Ye, C., Wang, P., Yasir, M.: Improved yolov5 network method for remote sensing image-based ground objects recognition. Soft Comput. (2022). https:\/\/doi.org\/10.1007\/s00500-022-07106-8","journal-title":"Soft Comput."},{"key":"2982_CR26","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2112.10992","author":"X Shu","year":"2022","unstructured":"Shu, X., Yang, J., Yan, R., Song, Y.: Expansion-squeeze-excitation fusion network for elderly activity recognition. IEEE Trans. Circuits Syst. Video Technol. (2022). https:\/\/doi.org\/10.48550\/arXiv.2112.10992","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"9","key":"2982_CR27","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1007\/s00371-019-01775-7","volume":"36","author":"P Xi","year":"2020","unstructured":"Xi, P., Guan, H., Shu, C., Borgeat, L., Goubran, R.: An integrated approach for medical abnormality detection using deep patch convolutional neural networks. Vis. Comput. 36(9), 1869\u20131882 (2020). https:\/\/doi.org\/10.1007\/s00371-019-01775-7","journal-title":"Vis. Comput."},{"key":"2982_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2928540","author":"J Tang","year":"2019","unstructured":"Tang, J., Shu, X., Yan, R., Zhang, L.: Coherence constrained graph LSTM for group activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2019). https:\/\/doi.org\/10.1109\/TPAMI.2019.2928540","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2982_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02653-5","author":"P Wang","year":"2022","unstructured":"Wang, P., Wang, M., He, D.: Multi-scale feature pyramid and multi-branch neural network for person re-identification. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02653-5","journal-title":"Vis. Comput."},{"issue":"6","key":"2982_CR30","doi-asserted-by":"publisher","first-page":"3300","DOI":"10.1109\/TPAMI.2021.30509182","volume":"44","author":"X Shu","year":"2021","unstructured":"Shu, X., Zhang, L., Qi, G.-J., Liu, W., Tang, J.: Spatiotemporal co-attention recurrent neural networks for human-skeleton motion prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3300\u20133315 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.30509182","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2982_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02673-1","author":"X Yao","year":"2022","unstructured":"Yao, X., Zhang, J., Chen, R., Zhang, D., Zeng, Y.: Weakly supervised graph learning for action recognition in untrimmed video. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02673-1","journal-title":"Vis. Comput."},{"key":"2982_CR32","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2982_CR33","doi-asserted-by":"publisher","unstructured":"Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180. https:\/\/doi.org\/10.48550\/arXiv.1805.10180 (2018)","DOI":"10.48550\/arXiv.1805.10180"},{"issue":"16","key":"2982_CR34","doi-asserted-by":"publisher","first-page":"3059","DOI":"10.3390\/rs13163059","volume":"13","author":"J Hu","year":"2021","unstructured":"Hu, J., Zhi, X., Shi, T., Zhang, W., Cui, Y., Zhao, S.: Pag-yolo: a portable attention-guided yolo network for small ship detection. Remote Sens. 13(16), 3059 (2021). https:\/\/doi.org\/10.3390\/rs13163059","journal-title":"Remote Sens."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02982-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02982-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02982-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T17:10:27Z","timestamp":1712337027000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02982-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,13]]},"references-count":34,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2982"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02982-z","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,13]]},"assertion":[{"value":"22 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}