In recent years, public safety has gradually become the focus of society. Generally, detecting the object requires specialized and expensive equipment, which is not easy to be widely deployed. Wi-Fi is generally used in wireless sensing as a low-cost, convenient, and harmless technology. Existing Wi-Fi-based object detection systems suffer from multipath effects and hardware defects, resulting in a significant amount of noise in the channel state information (CSI) extracted from the physical layer. In this article, we propose Wi-Tar, a novel system that utilizes the quotient of CSI value as the base signal. Wi-Tar analyzes their differences for different materials by the reconstructed CSI complex. For objects of the same material, we design a feature to eliminate the interference of object size and develop a multichannel integrated convolutional neural network and a long short-term memory (MCICNN-LSTM) structure. MCICNN-LSTM fully leverages the characteristics of multiple-input multiple-output (MIMO) technology, achieving high accuracy. We implement this system in commercial Wi-Fi devices and evaluate its performance in three indoor scenarios. Extensive experimental results demonstrate that the recognition accuracy of three materials is over 99.5% in a 2-m detecting distance, and the average recognition accuracy of objects of the same material exceeds 99%. Even when these objects are placed in different occlusions, Wi-Tar can still accurately distinguish them.