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 a 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.