In this study, an object detection algorithm is designed based on an improved feature extraction network to solve the shortcomings of low object detection accuracy and inaccurate object position detection. Initially, the training set is enhanced; subsequently, a two-path network is designed for usage in feature extraction of the Faster R-CNN algorithm; finally, the non-maximum suppression mechanism is improved in the prediction part of the algorithm, and the weighted averaging method is adopted for obtaining the positions of multiple similar prediction boxes. The experiments conducted using the VOC 2007 and VOC 2012 databases denote that the proposed algorithm outperforms the classical object detection algorithm, with an accuracy rate of 79.1% and an improvement of 3%-4%. Thus, the effectiveness of the algorithm is verified.