## 3 Algorithm Overview
The final results are assessed with benchmark results, and it is noticed that the proposed network out-performer with an improved ELU activation function and other units. Figure 7 shows the accuracy vs. loss comparison of our proposed network. The \"x-axis\" shows the number of training iterations, while the \"y-axis\" shows the training accuracy and loss. We set a low learning rate that helps us to reduce training loss very slowly. After analyzing the curves, it can be claimed that our proposed network achieves a good learning rate, as the \"red\" accuracy curve moves smoothly, and finally, it attains the best performance. A similar smoothness is achieved with the \"green\" loss curve. It can be observed that the loss is gradually decreasing and accuracy is improving alongside, until they become constant. It is the ultimate performance of the CSSA network. \"A\" is the proposed network, while \"B\" is SegNet, which is used as the benchmark result for analysis. It can be clearly observed that the proposed network is performing well with perfect converging loss and accuracy curves as compared to its counterpart. Figure 8 shows different road-scene training and testing set images and their corresponding results. The resultant images show clear boundary delineation among different object. It also shows a significantly efficient result regarding the detection of objects in a given image. Figure 8: CSSA image dataset results. Figure 7: CSSA vs. SegNet. Table 2 shows quantitative results on the CamVid dataset. Our proposed CSSA results are compared with other popular benchmark results. The road scene detection process comprises 11 road scene categories. The proposed CSSA outperforms over all other methods. There is a great deal of improvement in class average accuracy and global accuracy for the smaller classes. The Pascal VOC-12 [7] segmentation dataset is composed of the segmentation of a few salient object classes with usually a variable background class. It is different from earlier segmentation for road scene understanding benchmarks, which need to learn both classes and their spatial context. Table 3 presents the Pascal VOC-12 segmentation challenge results in comparison with established benchmark results. The proposed CSSA is intended for road scene understanding; therefore, we do not intend to benchmark this experiment to attain the top rank in the Pascal VOC-12 segmentation challenge. Therefore, the CSSA is not trained by using multi-stage training [17] or post-processing, which uses CRF-based methods [15; 16]. The CSSA network is trained end-to-end without any other assistance, and Table 3 presents the resultant performance. Early best-performing benchmark networks are either very large [15] and/or they use a CRF (such as [66] use Recurrent Neural Networks (RNNs)). In contrast, the key object behind CSSA implementation is to reduce the number of trainable parameters and improve overall inference time without other aids. We have reported results in terms of class average accuracy and the size of the network. There is little loss recorded in the performance due to dropping fully-connected layers. However, this reduces the size of CSSA and offers us the capability to train the network end-to-end. Although it may be argued that bigger and denser CNNs can have outstanding performance, it is at the cost of increased memory requirements and longer inference time. This kind of architecture also leads to a complex training mechanism. Due to such kinds of bottlenecks, bigger and denser CNNs are not

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appropriate for real-time applications, such as road scene understanding. Therefore, the CSSA could be a preferred choice for the ADAS architecture. According to a report by [67], during 1990-2010, 54 million people were injured in roadside accidents. About 1.4 million deaths occurred since 2013, and in 68,000 cases, the victims were children less than five years old.