YOLOv6+: Simple and Optimized Object Detection Model for INT8 quantized inference on mobile devices

The You Only Look Once (YOLO) series stands out for its exceptional scalability, enabling seamless deployment on a variety of diverse software and hardware platforms. This scalability has driven its utilization in numerous industrial sites. Recently, there has been an increasing focus on developing quantization-friendly architectures, especially for INT8 inference, to support real-time processing on low-power devices such as mobile platforms. In this paper, we propose the simple and novel approach to enhance the performance of the YOLOv6 model, a widely used object detector in industrial applications, by incorporating skip connections in selected re-parameterization blocks to achieve a quantization-friendly architecture. In addition, we introduce an regression normalization method to address the performance degradation in the head part that often occurs during the TFLite INT8 conversion for mobile environments. The proposed YOLOv6+ architecture outperforms the original YOLOv6 and its successor YOLOv8 by achieving comparable speed in FP/INT8 precision inference while improving mAP performance and enhancing quantization-friendliness. Furthermore, the regression normalization method effectively mitigates performance degradation during TFLite INT8 conversion and is verified to be applicable to other recently developed YOLO series models.