## 1 Introduction  
Automotive component cracking is a common phenomenon in the mechanical components of an engine and has a significant impact on the structural firmness of engine parts, engine fuel consumption, and environmental pollution. Cracking is one of the signs of deficits in engine components, which can have many cases, such as mechanical stresses, wear mechanisms, overheating, and lubrication mechanisms. Manual crack checking and the inspection of automotive engine components is performed on a regular basis by trained and experienced machinists and mechanics who use a spray, a magnifying glass, and a magnetic particle inspection method [1, 2, 3], and this process is labor-intensive and time-consuming. Moreover, inspection outcomes rely immensely on human bias and subjective judgment, leading to inaccuracy and in some faults being overlooked. In the past couple of years, several image-based, automatic, or semiautomatic crack detection and classification approaches using traditional digital image processing approaches have been proposed, namely, thresholding, segmentation-based, and edge-detection-based approaches. [PERSON] et al. [4] proposed an automotive engine cylinder head crack detection method based on Canny edge detection with morphological dilation that employs pre-processing, de-noising using Gaussian filtering, calculation of the intensity gradient, double thresholding, non-maximal suppression, and morphological dilation. Generally, traditional crack detection approaches follow image graying, binarization, edge extraction, and morphological operations. Though traditional crack detection approaches are more effective than manual checking or inspections, they are incredibly dependent on modelsand processing flows. This dependence leads to poor generalization due to noise formed by structural dynamics, carbon deposits, and other factors related to the life of parts. ConvNets have recently been proposed for object detection and image classification in vision-based systems. Some of the ConvNet architectures, namely, VGGNet [5], InceptionV3 [6], AlexNet [7], ResNets [8], and GoogleNet [9], were typically trained on high-volume datasets, namely, GoogleNet and ImageNet, and have successfully been used to obtain state-of-the-art outcomes. Many recent works have established ConvNet-based algorithms for the purposes of automotive engine precision part defect detection [10], highway crack classification and detection [11], weld defect detection and image defect recognition [12], weld image on-line defect detection [13], andautomotive paint defect detection [14]. The YOLOv5 architecture [15] utilizes the entire image instantaneously for detection. Moreover, it is considerably faster than the R-CNN series of the framework. It has high precision, high speed, and fewer model parameters than the other object detector algorithms. Previous works have recommended original ConvNets constructed from scratch and trained to localize or sort defects or cracks in images. Training a new network is typically a costly practice that frequently checks and examines various ConvNet algorithms and tunes several parameters to optimize performance. Thus, transfer learning (TL) of a pre-trained deep learning (DL) architecture on standard datasets for new baseline classification tasks has been implemented. Ref. [16] proposed an image-based approach using TL to detect concrete cracks, Ref. [17] proposed a DL-based transfer learning approach to detect cracks in ceramic plates, Ref. [18] proposed a DL-based approach to detect defects in tire X-ray images, and so on. in this work, we first developed a ConvNet architecture-based approach to identify and classify cracks using images of automotive engine components that are among the most susceptible to cracks in their engine parts. The datasets were collected and preprocessed. A ConvNet-based model was assembled from scratch to build the primary crack classifier.