Small Object Detection Based on Modified FSSD and Model Compression

TL;DR

This paper introduces a small object detection algorithm based on an improved FSSD architecture combined with model pruning, achieving high accuracy and speed while reducing computational costs.

Contribution

It proposes a novel small object detection method utilizing feature fusion and model pruning to enhance accuracy and efficiency.

Findings

Achieved 80.4% mAP on PASCAL VOC

Detection speed of 59.5 FPS on GTX1080ti

Compressed model maintains 79.9% mAP after pruning

Abstract

Small objects have relatively low resolution, the unobvious visual features which are difficult to be extracted, so the existing object detection methods cannot effectively detect small objects, and the detection speed and stability are poor. Thus, this paper proposes a small object detection algorithm based on FSSD, meanwhile, in order to reduce the computational cost and storage space, pruning is carried out to achieve model compression. Firstly, the semantic information contained in the features of different layers can be used to detect different scale objects, and the feature fusion method is improved to obtain more information beneficial to small objects; secondly, batch normalization layer is introduced to accelerate the training of neural network and make the model sparse; finally, the model is pruned by scaling factor to get the corresponding compressed model. The experimental results show that the average accuracy (mAP) of the algorithm can reach 80.4% on PASCAL VOC and the speed is 59.5 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. The algorithm can not only improve the detection accuracy of small objects, but also greatly improves the detection speed, which reaches a balance between speed and accuracy.