CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving

In autonomous driving, traditional Computer Vision (CV) agents often struggle in unfamiliar situations due to biases in the training data. Deep Reinforcement Learning (DRL) agents address this by learning from experience and maximizing rewards, which helps them adapt to dynamic environments. However, ensuring their generalization remains challenging, especially with static training environments. Additionally, DRL models lack transparency, making it difficult to guarantee safety in all scenarios, particularly those not seen during training. To tackle these issues, we propose a method that combines DRL with Curriculum Learning for autonomous driving. Our approach uses a Proximal Policy Optimization (PPO) agent and a Variational Autoencoder (VAE) to learn safe driving in the CARLA simulator. The agent is trained using two-fold curriculum learning, progressively increasing environment difficulty and incorporating a collision penalty in the reward function to promote safety. This method improves the agent's adaptability and reliability in complex environments, and understand the nuances of balancing multiple reward components from different feedback signals in a single scalar reward function. Keywords: Computer Vision, Deep Reinforcement Learning, Variational Autoencoder, Proximal Policy Optimization, Curriculum Learning, Autonomous Driving.

Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model

According to Global Electricity Review 2022, electricity generation from renewable energy sources has increased by 20% worldwide primarily due to more installation of large green power plants. Monitoring the renewable energy assets in those large power plants is still challenging as the assets are highly impacted by several environmental factors, resulting in issues like less power generation, malfunctioning, and degradation of asset life. Therefore, detecting the surface defects on the renewable energy assets would facilitate the process to maintain the safety and efficiency of the green power plants. An innovative detection framework is proposed to achieve an economical renewable energy asset surface monitoring system. First capture the asset's high-resolution images on a regular basis and inspect them to detect the damages. For inspection this paper presents a unified deep learning-based image inspection model which analyzes the captured images to identify the surface or structural damages on the various renewable energy assets in large power plants. We use the Vision Transformer (ViT), the latest developed deep-learning model in computer vision, to detect the damages on solar panels and wind turbine blades and classify the type of defect to suggest the preventive measures. With the ViT model, we have achieved above 97% accuracy for both the assets, which outperforms the benchmark classification models for the input images of varied modalities taken from publicly available sources.