基于深度学习的小目标检测综述.

Semantic segmentation algorithms can classify images at the pixel level, and are widely used in fields such as unmanned driving, medical image processing, and industrial automation, and have important research value. The research of semantic segmentation algorithms focuses on three aspects: improving the accuracy of segmentation, reducing the amount of parameter s and increasing the speed of inference. The lightweight semantic segmentation algorithm ENet uses a multi-layer convolutional codec and a large number of dilated convolutions to avoid excessive downsampling and use of spatial information. Although it retains some spatial information integrity and large receptive field, the codec is bloat-ed, the transmission of spatial information is poor, and the sensory field overflows and causes gr id effect. Aiming at the above problems, this paper tailors the EN et algorithm structure, uses the attention mechanism and the pyramid dilated convolution to design spatial information transmission module, optimizes the algorithm structure, improve s the algorithm receptive field, and completely transmits the spatial information transmission. The experimental results on public datasets Cityscapes and BDD100K show that the new module can improve the performance of the original algorithm with a smaller amount o f parameters and calculations, which proves the redundancy of t he original algorithm and the effectiveness of the designed module. [ABSTRACT FROM AUTHOR]