Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD

Blog Article

In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object Rocker Arm detection, a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed.Firstly, the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure; at the same time, the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel.Then, the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps, and the scale-invariant convolution is performed again on these layers.Finally, the obtained layer is used Hockey Accessories - Elastic for detection and localization.The final location coordinates and confidence are output after nonmaximum suppression.

Experimental results on the Pascal VOC 2007 and 2012 datasets confirm that there is a 8.2% improvement in mAP compared to the original SSD and some existing algorithms.