## 4 Discussion
Figure 13: The processed photos, which were taken with different exposure values(EVs)—2 EV, 0 EV, and \(-4\) EV—(original photos can be found in [39]): (**a**) brightness distribution; (**b**) “contrast” (0,40) transformation—image modified by applying “contrast” function with cutoff parameters (0,40); (**c**) “contrast” (40,0) transformation—image modified by applying “contrast” function with cutoff parameters (40,0). Addressing extreme lighting variations, such as sudden transitions from low to high brightness, presents an interesting direction for future research. Comparing the model's performance in real-world scenarios with unpredictable lighting changes during computer vision tasks could provide valuable insights for further improvements. The averaging fusion method provides more stable results compared to the dense fusion method. The averaging fusion method helps avoid local minima and balances results across backbones. However, the dense fusion method can achieve slightly better results with SMA on the original O-SMA dataset due to the additional configuration of weights determining a higher backbone impact (which was investigated and observed previously [31]). This allows different backbones to learn different features independently but can increase model instability in SMA design, as indicated by the higher standard deviation values measured during the CV study. In general, the experimental results for the MobileNetV3 Small architecture with MBs provide valuable insights into the effect of SMA, especially for potential applications in Edge Computing. The computational cost of EC/EI devices is a very important aspect for practical applications, and it should be investigated and evaluated by metrics like inference time, memory usage, and energy consumption. For this reason, specific aspects of EC/EI hardware should be taken into account to justify the feasibility of the approach proposed. However, this study's primary aim at this stage was to investigate the general feasibility of the proposed approach without imposing constraints tied to specific hardware. The characteristics of EC/EI hardware must be considered in future evaluations to validate the practicality of our method. Hardware-specific experiments are planned as the next stage of this research, which will involve implementing the proposed SMA-MB approach in EC/EI devices. The current results suggest that the SMA-MB method is theoretically promising and likely to improve model performance in appropriate hardware architectures, paving the way for further investigation. The proposed approach to modifying the NN architecture offers several benefits. It may improve query performance speed (QPS) and timing results by leveraging multiple parallel processes or processors. Additionally, this method allows for scaling the model to meet user needs, balancing inference time, available computational resources, and target accuracy. MB architectures in DNNs offer significant potential for addressing the constraints of EI hardware, such as limited processing power, memory constraints, power consumption, and the need for real-time processing. By utilizing a multiprocessor approach, optimized models composed of several lightweight backbones can employ designs and SMA techniques that reduce computational load and power usage while maintaining accuracy, making them ideal for resource-limited edge devices. Efficient memory utilization and low-latency processing capabilities are critical for real-time applications, ensuring responsive and reliable performance. SMA not only decreases the computational load but also potentially increases robustness across different camera settings and lighting conditions. By pursuing these research directions, MB architectures with SMAs can be further optimized to meet the stringent requirements of EI hardware, driving advancements in edge AI solutions. However, the impact of various SMAs techniques has not yet been fully evaluated. The main challenge in Edge Computing is limited computing resources.