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Small objects have relatively low resolution, the unobvious visual featureswhich are difficult to be extracted, so the existing object detection methodscannot effectively detect small objects, and the detection speed and stabilityare poor. Thus, this paper proposes a small object detection algorithm based onFSSD, meanwhile, in order to reduce the computational cost and storage space,pruning is carried out to achieve model compression. Firstly, the semanticinformation contained in the features of different layers can be used to detectdifferent scale objects, and the feature fusion method is improved to obtainmore information beneficial to small objects; secondly, batch normalizationlayer is introduced to accelerate the training of neural network and make themodel sparse; finally, the model is pruned by scaling factor to get thecorresponding compressed model. The experimental results show that the averageaccuracy (mAP) of the algorithm can reach 80.4% on PASCAL VOC and the speed is59.5 FPS on GTX1080ti. After pruning, the compressed model can reach 79.9% mAP,and 79.5 FPS in detection speed. On MS COCO, the best detection accuracy (APs)is 12.1%, and the overall detection accuracy is 49.8% AP when IoU is 0.5. Thealgorithm can not only improve the detection accuracy of small objects, butalso greatly improves the detection speed, which reaches a balance betweenspeed and accuracy.

Small Object Detection Based on Modified FSSD and Model Compression