In order to improve the adaptability of intelligent vehicles to quickly detect objects in various scenes, a joint method of multi-task sharing the same feature extraction network is proposed. First, ResNet-50 network is used to extract image features of the encoder. Then, multi-scale feature prediction and fast regression in single shot multibox detector target detection algorithm are used to decode the detection results. A pyramid pool structure of porous space in DeepLab v3 is used to process the multi-scale mapping, bilinear sampling and batch normalization of the image features after ResNet-50 sampling so as to complete segmentation and decoding. Finally, the training of the joint method is completed under the set training parameters. Experimental results show that the mean average precision of the method is 89.00%,the mean intersection over union is 83.0, and the number of frames per second is 31 frame, which can support intelligent vehicle to complete certain tasks.