With the rapid development of machinery and equipment modernization, more and more non-standard shaped parts are designed and put into specific occasions to use to meet the needs of special circumstances. Therefore, how to quickly recognize the shaped parts has become an urgent need for a technology. To recognize shaped parts, deep learning methods such as the widely used YOLOv5s network are commonly employed. However, directly deploying the official network model has drawbacks, including heavy reliance on data, poor detection results for small target objects, and high hardware requirements. These issues increase the threshold for non-professionals to use it. For this reason, this paper designs an improved network based on YOLOv5s. This paper proposes improvements in terms of both lightness and accuracy. In terms of light weight, the backbone of YOLOv5s is replaced by MobileNetV3; and the convolution and C3 module of the head part of YOLOv5s is replaced by phantom convolution and C3Ghost module, and the attention mechanism layer is trimmed to reduce the number of computational parameters and model size. In terms of accuracy, non-maximum suppression (NMS) is improved to Soft-NMS; intersection over union (IoU) loss function is replaced with distance-IoU loss function. And trained on the homemade shaped parts dataset, the results show that the average accuracy of the improved network is 99.2% in the test case, the model size is 2.4M, and the detection time is 1.5 ms per image, which is a significant increase in speed and accuracy compared with other unmodified networks, and a substantial decrease in the model size and the number of parameters.