The study Model for Helmet Detection using PNN (Probabilistic Neural Network) and RNN (Recurrent Neural Network) employs advanced neural network methodologies, specifically PNN and RNN, to enhance the accuracy of detecting helmets in images or videos. By leveraging ensemble learning techniques, including lime, the model combines the strengths of probabilistic and sequential data processing methods to improve predictive accuracy. Through lime integration during training, the model gains interpretability insights, aiding in understanding the complex decision-making process. This facilitates the identification of critical features contributing to helmet detection, guiding preprocessing steps such as image standardization and feature extraction. The individual PNN and RNN classifiers are trained on labeled image data, and lime analysis enhances their interpretability, shedding light on their decision processes. Performance assessment metrics like precision, recall, and F1-score are utilized to estimate the efficiency of the classifiers. By aggregating predictions from PNN and RNN, leveraging lime's interpretability, the model harnesses their collective intelligence to improve overall detection performance. This approach ensures robustness and reliability in detecting helmets, contributing to safety compliance monitoring and accident prevention in various environments.