A Novel Bird Detection and Identification based on DPU processor on PYNQ FPGA

In this paper, deep learning bird identification is proposed and implemented on PYNQ FPGA with SoC architecture. The new detection method can be divided into moving object detection, and neural network processor architecture. The moving object detection is based on the principle of frame difference to obtain the image label. The recorded frames after being processed through morphology, fuzzy and binarization result in the moving object detected with its size and position within the image. The confirmed moving object is pushed through a deep-learning processor unit (DPU) for classification, resulting in the type of the bird. The results of the experiment show that the proposed method can reach 84.3% accuracy with 126.8 GOP/s/W power efficiency, which is very suitable for low power surveillance experiments in forests or outdoor environments.