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The detection of microorganisms like bacteria in water is extremely important and challenging. Colony detection is an effective solution because visible colonies can be formed by bacteria in water. Aiming at the characteristics of small targets and regular shape in RGB image, a cascade network combining SSD_MOBILENET_V1_FPN with SVM HOG classifier is designed to detect and count the colonies with high accuracy, both error rate and missing rate were less than 3%. To solve the performance bottleneck, a on-chip system device Zynq UltraScale+ MPSoC EV is applied to accelerate colony count cascade network based on FPGA which could support AI computing acceleration, it can detect and count 10 colony images per second with the advantage of portability and low power consumption.
Shousheng Liu,Zhigang Gai,Mei Zhang,Fengxiang Guo,Xu Chai,Yibao Wang,Ding Hu,Shaoyan Wang,Lili Zhang,Xueyu Zhang,Zhigang Chen,Xiaoling Sun, andXin Jiang
"Small target detection method with high accuracy for visible colony RGB image formed by bacteria in water", Proc. SPIE 11767, 2020 International Conference on Optoelectronic Materials and Devices, 117671D (26 January 2021); https://doi.org/10.1117/12.2592262
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Shousheng Liu, Zhigang Gai, Mei Zhang, Fengxiang Guo, Xu Chai, Yibao Wang, Ding Hu, Shaoyan Wang, Lili Zhang, Xueyu Zhang, Zhigang Chen, Xiaoling Sun, Xin Jiang, "Small target detection method with high accuracy for visible colony RGB image formed by bacteria in water," Proc. SPIE 11767, 2020 International Conference on Optoelectronic Materials and Devices, 117671D (26 January 2021); https://doi.org/10.1117/12.2592262