Priority Intra-model Adaptation for Traffic Sign Detection and Recognition

Generic object detection models find wide usage in various fields, but their direct application as generic object detectors is often suboptimal, prompting artificial intelligence application engineers to seek alternative approaches. Researchers in different domains invest considerable time and resources to adapt these generic object models to their specific applications, enhancing their suitability and effectiveness in diverse contexts. To expedite the adaptation from generic object detection to specific object detection in traffic sign detection and recognition (TSDR), we introduce the Priority Intra-model Adaptation (PIA) guiding principle. Diverging from the conventional approach of appending numerous additional components to specialized models, PIA emphasizes prioritizing frame optimization as the initial step in the adaptation procedure. In this study, based on the PIA, we present a comprehensive set of standardized adaptation procedures for TSDR. Through extensive experiments on TT100K and GTSDB datasets, we demonstrate the effectiveness of PIA in achieving state-of-the-art performance for specific object detection models within the TSDR domain by strictly adhering to the proposed specification.