In industrial settings, with densely arranged and distributed industrial parts, the use of horizontal box object detection often leads to issues, such as incorrect selection, missing parts, and loss of boundary direction. In this study, we propose a rotating workpiece object detection algorithm based on an enhanced version of YOLOv5s. First, a free parameter SimAM network is introduced to prioritize crucial information without increasing the number of model parameters. This enhancement enhances feature extraction in complex backgrounds and mitigates noise interference. Second, the original complete intersection over union (CIoU) regression function is replaced with the SIoU function, which incorporates an angle factor, aligning more with the rotation box detection. Substituting the activation function with Mish further enhances the model's convergence speed and accuracy. The algorithm introduces the phase-shifting coding method and an improved HardL-Tanh activation function to realize the prediction of angle and regression angle cosine values, thereby overcoming the angle multiuniformity and boundary problems associated with the five-parameter representation method and realizing the rotation frame detection of the workpiece. Experimental results demonstrate a mean accuracy precision of 97.4%, highlighting the proposed algorithm's advantages, including smaller weight files, higher average accuracy, and reduced prediction time. These qualities align with the real-time requirements of industrial applications.