Aiming at the problems of large amount of network parameters and low detection accuracy of the existing YOLOseries object detectorsa highperformance universal object detector named YOLOv7TT is proposed based on YOLOv7tiny model.FirstlyGeneralized and Friendly ELAN (GFELAN) module is introduced into Backbone and Neck networks to expand the width and depth of the network and eliminate the redundant features generated by the networkso as to reduce the parameter quantity and computation cost.Thenthe improved SimOTA sample allocation method is used to optimize the allocation of positive samples in the training process and accelerate the convergence speed of the network.Finallythe knowledge distillation method is used to distill and train the model to improve its detection accuracy while ensuring lightweight.The experimental results show that:1) Compared with YOLOv7tinyYOLOv7TT reduces the quantity of network parameters by 11% and 9.7%and improves the AP by 4.2 and 3.0 percentage points respectively on the VOC2007 and COCO2017 datasets;and 2) The model detection accuracy is further improved by using knowledge distillationthe AP reaches 59.4% (with 5.3 percentage points improved) on the VOC2007 datasetwhich effectively solves the problems of large quantity parameters and low detection accuracy.