## 3 Results and Discussion
### Reasons behind the Failures of the Contemporary Approaches
#### 3.2.2 Samples with Less Divergence
Custom Object CNN's poor identification accuracy may also be due to its present implementation architecture. However, this architecture is closely related to similar studies conducted on geophysical image-processing detection [18; 25]. Additionally, we have compared the best-in-class object detection models in Table 1 and found results inferior to our proposed method. Hence, we firmly believe that the identification accuracy cannot be improved any further in the absence of large samples and high-quality training image samples. 4. We also tested with the current input samples after converting the grayscale images to two predefined colours, reducing the information within the image from 256 grey shades to only 2. This procedure is known as binarization, and it often provides sharper and clearer contours of different objects in an input image [36]. However, the resulting accuracy was still found to be unsatisfactory because of the tiny sample size and unidentifiable shape-related features, making it difficult to train the Custom Object CNN model to accurately identify the irregular-shaped micro-pores and fractures. In contrast, the proposed pixel-wise \(k\)-IN shows satisfactory accuracy in the presence of binarised input. 2. Learning the shapes of an individual pore and a fracture [18]: For this task, we extracted the images of the \(\mathcal{H}_{i}\) from 400 micro-image samples as described in the work of [18] and created a training set of five shapes (such as intra-particle, vuggy, moldic, biomoldic and fracture [18]) with 50 examples of each category for the deep learning models for feature extraction and found considerably low detection accuracy because of the high geometric and visual similarity (insignificant dissimilarity) in the shapes of different types of pores and fractures in the used micro-image samples. The extracted micro-pores and fractures have inferior quality and remarkably similar shapes because of their tiny sizes. Moreover, the quality of the used grayscale image samples prevents the 'Image Pro Plus' software from extracting the high-resolution images of pores and fractures that could further be used as training samples for the Custom Object CNN. Consequently, the Custom Object CNN model fails to extract the above features that help perform the porosity classifications and fracture identifications uniquely. We found that the training grayscale image samples of micro-pores and fracture images (extracted using the 'Image Pro Plus') are highly distorted (irregularly shaped geometry) in most cases and subsequently, extracting their shape geometry-related features became extremely difficult in reality for CNN-based object detection systems, which are square bounding-based detection, whereas pores are generally irregularly shaped. These pores can sometimes be much smaller than the size of the convolution filters chosen, which leads to false positives. Hence, the identification accuracy of this strategy, only approximately 10%, is very inferior to that of case (i). In contrast, the proposed approach shows better accuracy than the above deep learning approaches because of the benefit of its pixel-level in-depth analysis and the adoption of an exhaustive search approach to identify new neighbouring dark pixels of a detected dark pixel.