An Improved YOLOv12n for Enhancing Small-Scale and Occluded Face Detection in Distributed Edge Computing

Face detection in distributed edge computing is confronting challenges such as loss of facial features due to occlusions, insufficient feature of small-scale faces, and difficulty in efficient deployment on distributed terminals. In this paper, an improved you only look once version 12 nano (YOLOv12n) is proposed, specifically designed to promote the detection of occluded and small-scale faces in distributed edge computing. A spatial-to-depth convolution (SPD-Conv) is introduced to optimize feature extraction and reconstruct feature encoding. A cross stage partial receptive field enhancement module (C3RFEM) is integrated to strengthen modeling capability for occluded and small-scale faces by multi-branch dilated convolutions. A multi-patch Monte Carlo attention (MPMCA) is proposed to elevate robustness and adaptability by random perturbations. A shape-aware normalized Wasserstein distance (SA-NWD) is adopted to increase the accuracy of localization and shape modeling for small targets. Experimental results demonstrate the proposed model achieves improvements in mean average precision 50 (mAP50) with increase of 0.92%, 1.20% and 3.44% on the Easy, Medium and Hard subsets of the WiderFace dataset compared to the original YOLOv12n, with enhanced robustness and adaptability in distributed edge computing.