Breast cancer is one of the most common tumors among women in the world, and its early screening is crucial to improve the survival rate of patients. Breast ultrasound, with the characteristics of non radiation, real-time imaging and easy operation, has become a common method for breast cancer detection. However, this method has some problems, such as low imaging quality and strong subjectivity of diagnosis results, which affect the accurate diagnosis of breast cancer. With the ongoing advancement of deep learning technology, intelligent breast cancer detection systems have effectively overcome these challenges, enhancing diagnostic accuracy and efficiency. This study uses nine popular deep learning object detection networks (including two-stage, one-stage, anchor-based, and anchor-free networks) for the detection of breast lesions and compares the results of these methods. The experiments show that the anchor-based Single Shot MultiBox Detector (SSD) network excels in overall performance, while the anchor-free Fully Convolutional One-stage Object Detector (FCOS) exhibits the best generalization ability. Moreover, the results also indicate that, in the context of breast lesion detection, anchor-based networks generally outperform anchor-free networks.