An improved YOLOv3 model based on skipping connections and spatial pyramid pooling

Abstract

The cascaded deep-learning network of YOLOv3 emphasizes on the layer-wise feature extraction. It neglects the sequential influence among the layers that contributes to the subtle features for the objects detection. An improved YOLOv3 model with skipping connections is proposed in this paper for the sufficient utilization of layer-wise features. Firstly, a DenseBlock network is adopted as the fourth and fifth down-sampling layers of YOLOv3. The DenseBlock is characterized of a parallel architecture and capable of transmitting the features backwards among extraction layers. Then, the features of preceding layers are incorporated by a skipping fashion into subsequential layers. Secondly, a spatial pyramid pooling (SPP) module is introduced at the neck of the object detector. It realizes the size-tuning of the model input. Then the multi-scaled region features are generated after the pooling and concatenation operation of the SPP. Finally, the validation experiments have been conducted on a dataset of helmet objects. The results have shown that the proposed YOLOv3 model improves the accuracy effectively. It yields a mean average precision 88.6% on the helmet detection, which is 3.5% higher than the original YOLOv3 network.

Keywords

• artificial intelligence
• neural networks
• cognitive systems