A Learning-rate Optimization Technique for Object Detection Accuracy Enhancement | Chapter 14 | New Approaches in Engineering Research Vol. 14
Because of their specific capability for Image Recognition, Deep Learning [1] models have recently been used primarily in Object Detection algorithms. These models extract features from input images and videos [2] to identify items present. Image processing, video analysis, speech recognition, biomedical image analysis, biometric recognition, iris recognition, national security applications, cyber security, natural language processing [3], weather forecasting applications, renewable energy generation scheduling, and other applications are all possible with these models. These models use the Convolution Neural Network (CNN) [3, which consists of several artificial neuron layers. The learning rate, the training batch size, the validation batch size, the activation function, and the drop-out rate are all aspects that influence the accuracy of Deep Learning models. These parameters are known as hyper-parameters. The accuracy of Object Detection is determined by the Hyper-Parameters used. F...