Multi-scale giant panda face detection based on the improved VGGNet architecture
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Abstract:
Individual identification of giant pandas is very important for studying their population of them.. Giant panda face detection is the first key step of giant panda individual identification method based on facial images. To solve the problem that the precision of the existing giant panda face detection methods are low, a multi-scale giant panda face detection method based on improved VGGNet-16 architecture was proposed in this paper. Firstly, based on the VGGNet-16 network architecture, a new feature extraction backbone network was constructed through certain improvements such as adding the residual block and BN(Batch Normalization) layer, reducing the channel dimensionality of convolution layer and adopting LeakyRelu active function as well. Secondly, a 3-scale feature pyramid network structure was combined with SPP(Spatial Pyramid Pooling) structure for object detection. Finally, the conventional convolution architecture was replaced with the depwise separation convolution architecture. Experimental results show that the proposed method can achieve 99.48% mAP(mean average recision) in the test dataset, and the detection performance is better than YOLOv4(You Only Look Once Version 4).
