Abstract
Aiming at the problem that the object detection accuracy of SSD algorithm in warehouse environment is not too high, a warehouse object detection algorithm based on improved SSD that fusion of DenseNet and SSD is proposed (Dense-SSD). Firstly, a large number of images containing cargos, trays and forklifts in real warehouse environment are collected through the camera, and the collected images are labeled to building the warehouse object dataset. Further the based network of the improved algorithm based on SSD pipeline is adapted with DenseNet, the Dense-SSD is trained from scratch on the PASCAL VOC and self-built warehouse object dataset, respectively. Finally, the trained models are tested on the above two datasets respectively. Experimental results show that the proposed method can reach 77.62% mAP on the PASCAL VOC, which is higher than SSD by 5.15 points. And the Dense-SSD can reach 93.85% mAP on the self-built warehouse object dataset while the model size is only 62.9 MB, which is higher than SSD by 1.43 points. Meanwhile, the model size is reduced by 31.8 MB than SSD.
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Acknowledgments
The work in this paper is supported in part by the Innovation Group Major Research Project of Guizhou Provincial Department of Education (QIAN JIAO HE KY ZI [2018]018), in part by the Research Foundation Project of Guizhou Minzu University (2017YB065), in part by the Foundation Research Project of College of Humanities & Sciences of Guizhou Minzu University (18rwjs016). We would like to thank the previous researchers for their outstanding achievements. Thanks for Professor Wang’s instruction and help.
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Chen, L., Wang, F., Wang, L., Wang, L. (2019). Research on Warehouse Object Detection Algorithm Based on Fused DenseNet and SSD. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_57
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DOI: https://doi.org/10.1007/978-981-13-9917-6_57
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