Utilizing a Hybrid Model for Human Injury Severity Analysis in Traffic Accidents

Road safety has been prioritized by governments globally, resulting in the implementation of numerous initiatives aimed at curtailing traffic accidents. Despite these efforts, the complete eradication of accidents remains unattainable. Therefore, swift and accurate responses to accident sites, accompanied by appropriate medical aid, are paramount in saving lives. Existing systems, primarily designed to alert medical personnel in the aftermath of an accident, rely solely on Vehicle Damage (V d ) to assess accident severity, neglecting Human Injury (H i ) considerations. This study proposes a hybrid model equipped with an attention mechanism, designed to classify accident severity based on both V d and H i . The proposed model accepts video or image inputs and classifies accident severity levels accordingly. Moreover, an extension of the model has been developed to obfuscate sensitive areas in accident imagery based on severity, particularly when such images are disseminated on public platforms without obtaining necessary consent. The proposed hybrid model, therefore, not only facilitates a more comprehensive severity assessment of traffic accidents but also ensures the protection of privacy and promotes ethical image sharing practices.