A lot of people suffer from disability and death due to unintentional road accidents, which also result in the loss of a significant amount of financial assets. Several essential features of Advanced Driver Assistance Systems (ADAS) are being incorporated into vehicles by researchers to prevent road accidents. Lane marking detection (LMD) is a fundamental ADAS technology that helps the vehicle to keep its position in the lane. The current study employs Deep Learning (DL) methodologies and has several research constraints due to various problems. Researchers sometimes encounter difficulties in LMD due to environmental factors such as the variation of lights, obstacles, shadows, and curve lanes. To address these limitations, this study presents the Encode-Decode Instant Segmentation Network (EDIS-Net) as a DL methodology for detecting lane marking under various environmental situations with reliable accuracy. The framework is based on the E-Net architecture and incorporates combined cross-entropy and discriminative losses. The encoding segment was split into binary and instant segmentation to extract information about the lane pixels and the pixel position. DenselyBased Spatial Clustering of Application with Noise (DBSCAN) is employed to connect the predicted lane pixels and to get the final output. The system was trained with augmented data from the Tusimple dataset and then tested on three datasets: Tusimple, CalTech, and a local dataset. On the Tusimple dataset, the model achieved 97.39% accuracy. Furthermore, it has an average accuracy of 97.07% and 96.23% on the CalTech and local datasets, respectively. On the testing dataset, the EDIS-Net exhibited promising results compared to existing LMD approaches. Since the proposed framework performs better on the testing datasets, it can be argued that the model can recognize lane marking confidently in various scenarios. This study presents a novel EDIS-Net technique for efficient lane marking detection. It also includes the model's performance verification by testing in three different public datasets.
A series of 1, 2, 4-triazole derivatives bearing piperidine moiety has been introduced as new anti-diabetic drug candidates with least cytotoxicity. p-Chlorophenylsulfonyl chloride (1) and ethyl nipecotate (2) were the starting reagents that resulted into corresponding 3,4,5-trisubstituted-1,2,4-triazole (6) through a series of steps. A series of electrophiles, 9a-e, were synthesized by reacting 4-bromobutyryl chloride (7) with differently substituted aromatic amines (8a-e) under basic aqueous medium. Target derivatives, 10a-e, were synthesized by the reaction of compound 6 with N-aryl-4-bromobutanamides (9a-e) in an aprotic solvent. Structures of all the derivatives were verified by spectroscopic analysis using IR, 1H-NMR, 13C-NMR and EIMS. Most of the derivatives revealed moderate to good α-glucosidase inhibitory activity with reference to acarbose. The moderate hemolytic potential demonstrated least toxicity.