ABSTRACT In this paper, a visual attention network (VANet) that draws inspiration from the complex workings of the human visual system is presented. The human eye possesses an extraordinary capacity to selectively concentrate on particular features within a scene while adjusting to varying light conditions. The proposed model replicates this ability by focusing on essential spatial and channel‐specific aspects of the input image. This is accomplished through an advanced architecture that incorporates attention mechanisms within residual learning blocks, which enhances the model's aptitude for recognizing crucial details while lessening the impact of irrelevant information. To further increase the effectiveness of the model, particularly in situations with changing illumination, a Luminance Enhancement Module is integrated. This module is intended to dynamically modify and improve brightness and contrast, enabling the network to operate efficiently in different environmental settings. By emulating how humans respond to variations in light, we strive to develop a more resilient and reliable visual perception system. This cutting‐edge model not only seeks to mimic the mechanical functions associated with human sight but also improves the vision tasks performed by machines, thereby expanding their use in fields like robotics and automated systems. Furthermore, we introduce a novel method that combines the functionalities of VANet with the human visual system with attention mechanism (HEVAM) for the detection and classification of flowers with remarkable accuracy. The synergy of these approaches results in precise image detection and classification, establishing a new benchmark for performance in botanical recognition tasks. These innovations propel the advancement of improved machine vision systems, enabling them to better understand and interact with their surroundings. The system proposed in this study demonstrated an accuracy of above 95% in the Flower Recognition (5 classes), Flowers dataset (16 classes), Oxford‐17 Flower (17 classes), and Oxford‐102 Flower (102 classes), surpassing the benchmark results.
Singh et al. (Sun,) studied this question.