Research and Application of Online Video Target Detection Based on Deep Learning
The complex and ever-changing background of the oil field,coupled with high camera suspension,results in a smaller proportion of objects in the monitoring image,increasing the difficulty of detection.Starting from the actual oilfield scenar-io,the low accuracy of SSD algorithm in detecting small targets is deeply studied and improved.The RP-SSD algorithm is proposed by adding an upsampling module and a prediction module in the feature pyramid to better fuse the feature maps generated by the front and back convolutional layers.Hollow convolution is used to expand the receptive field of the front convolutional layers,im-proving the accuracy of small target detection.Pascal VOC is used to validate the effectiveness of the improved algorithm,and fast R-CNN,SSD300,and DSSD321 are selected as control experiments.The experimental results show that RP-SSD significantly im-proves its performance in small object detection and can meet the requirements of real-time detection.
small target detectionfeature pyramidresidual networkvoid convolution
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