Research on Bomb-fall Detection Method Based on Advanced Region-based Fully Convolutional Networks

The method of image difference was often used in the detection of bomb-fall in the wild large field of view.However,due to the complexity of the situation after the bomb landing,the detection of dense bomb-fall became a difficult problem.To deal with it,a unique dataset for detection of bomb-fall was constructed after sorting and classifying the images of bomb explosion.Then,we analyzed the feature extraction network,region proposal network,position-sensitive RoI pooling layer,and classification & regression layer of R-FCN and improved them.The modified network calls advanced region-based fully convolutional networks and is used for single-frame detection.A network model training method based on pruning is used instead of the training method that blindly tries several times to obtain optimal training results.The ablation experiment was carried out on the unique dataset for detection of bomb-fall.The final mAP(mean Average Precision)reached 83.73%,which achieved a good detection performance.Compared with other commonly used algorithms on the Pascal VOC dataset,the results show the effectiveness of the algorithm.