An Improved Object Detection of Image Based on Multi-task Learning
Lei Yang, Lijuan Lou, Xin Song, Jiangtao Chen, Xiaorui Zhou · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · 2022
Object detection has a wide range of applications in the real world. For example, accurate identification of object in autonomous driving is an important prerequisite for realizing safe autonomous driving. The current object detection models can't utilize more detailed semantic information and solve the problems of different objects in the image. This paper improves YOLOV5 by introducing semantic segmentation to structure a multi task framework. Convolutional Neural Network (CNN) is used to extract features from input image in this paper. The feature maps are shared by detection thread and segmentation thread to complete object detection and semantic segmentation. Experimental results on the Cityscapes dataset show that the improved model achieves better performance compared with some other models.