Small Object Detection for Mobile Behavior Recognition Based on Wasserstein Distance and Partial Convolution

While mobile phones offer convenience in our daily lives, they also introduce associated security risks. For instance, in high-security settings like confidential facilities, casual mobile phone usage and calls can inadvertently lead to the leakage of sensitive information. In response to such security concerns, this paper proposes an algorithm for recognizing mobile phone behaviors in high-resolution images with a wide field of view.To improve inference speed, we introduce the C3_Faster module. To address the challenge of detecting small-sized targets in images, we propose a boundary loss function. This reduces the scale sensitivity of IoU loss and mitigates model underperformance in detecting small objects. Experimental results demonstrate that, our improved algorithm achieved a 7.6% increase in mAP and a 38% improvement in inference speed. These findings highlight the effectiveness of our enhanced algorithm, making it well-suited for the task of mobile behavior recognition in secure environments.