Aimed at the problem of traditional semantic segmentation algorithms having large parameters and slow running time, which are not conducive to their practical application for contraband identification technology, this paper proposes a prohibited item identification algorithm based on a lightweight segmentation network. A dilated convolution module is used in a shallow feature layer of the model to enlarge the receptive field of the network, reduce misclassification, and improve segmentation precision. To reduce computational complexity, an asymmetric convolution module is used in a deep feature layer to replace the traditional single convolution operation. The experimental results show that the proposed algorithm achieves balanced performance for identification accuracy and speed, the mean intersection over union (mIoU) is 73.18×10 ^-2, and the frames per second rate (FPS) is 27.1.