基于改进的 SSD 监理目标检测研究.

Aiming at many problems caused by manual acceptance in decoration projects, this paper proposes an improved SSD algorithm and applies it to supervision work to replace manual acceptance and promote the realization of intelligent supervision. Since the SSD algorithm has problems such as re-examination of the same target and poor detection of small targets, the DPN network is used to replace the basic feature extraction network VGG16 in this paper. DPN combines the advantages of Resnet and Densenet and has better feature extraction ability. The feature maps are fused by weighted FPN to highlight the contributions of feature maps of different layers and enrich the semantics of feature maps for prediction. The use of depthwise separable convolution reduces the number of parameters of the model and improves the inference speed of the algorithm. The experimental comparison shows that the average accuracy of the improved model is increased by 3.47%, and the average accuracy of small number detection is improved by up to 15%, which proves that the new model has a good effect in the task of supervision target detection. [ABSTRACT FROM AUTHOR]