Abstract:

As autonomous vehicles increasingly rely on computer vision to navigate complex environments, achieving high accuracy in road semantic segmentation has become a critical challenge. This study proposes a novel approach utilizing layer-wise training to enhance the performance of semantic segmentation models specifically tailored for road scenes. By gradually fine-tuning each layer of a convolutional neural network (CNN), we aim to improve the model's ability to discern various road elements, such as lanes, vehicles, pedestrians, and traffic signs. Our approach begins with pretraining on a large-scale dataset, followed by a structured training process that sequentially optimizes individual layers while maintaining overall network integrity. We employ a series of performance metrics to evaluate the effectiveness of this method compared to traditional training strategies. Experimental results demonstrate significant improvements in segmentation accuracy and robustness, particularly in challenging conditions such as varying lighting and weather. The findings suggest that layer-wise training can be an effective strategy for enhancing semantic segmentation in autonomous driving applications, ultimately contributing to safer and more reliable vehicle navigation systems.

Keywords:
Camvid dataset,
enet algorithm

INTRODUCTION:

Approximate computing is an evolving paradigm that aims to improve the power, speed, and area in neural network applications that can tolerate errors up to a specific limit. This letter proposes a new multiplier architecture based on the algorithm that adapts the approximate compressor from the existing and proposed compressors’ set to reduce error in the respective partial product columns. Further, the error due to the approximation in the proposed multiplier is corrected using a simple error-correcting module. Results prove that the power and power–delay product (PDP) of an 8-bit multiplier is improved by up to 39.9% and 43.6% compared with the exact multiplier and 27.5% and 23.9% compared to similar previous designs. The proposed multiplier is validated using image processing and neural network applications to prove its effificency. IMAGE processing and neural network applications can tolerate a drop in accuracy up to a specific acceptable limit [1]. Therefore, precise operations are replaced with imprecise operations to overcome limitations, such as high power consumption and low speed in digital systems with accuracy being the main tradeoff. Multiplication is an essentialcomponent in neural network applications. Various researchers have proposed approximate multiplier architectures to obtain hardware savings. This letter proposes a new unsigned compressor-based adaptive approximate multiplier (CAAM) based on a new methodology for exploring compressor assignment in partial product reduction (PPR) structure to reduce the circuit
complexity without signifificantly compromising accuracy. The proposed work’s goal can be outlined as follows.

1) A new 4:2 approximate compressor circuit is proposed to reduce the circuit complexity at the PPR stage.
2) An algorithm has been proposed which selects the approximate compressor from the existing and the proposed compressors so as to reduce the error in the
respective partial product columns.
3) The error due to the approximation in the proposed CAAM