Table 1 A summary of the pros and cons of some of the related works

Zhao et al. [30]

Cropped image from Videos from manned Helicopter

> 1200 (256 × 256)

Pre-trained AlexNet using SVM with RBF kernel. (Caffe framework)

2

Required more feature engineering their model is limited as it does not include object detection of the faults

Liu et al. [31]

UAV images

> 1500 (500 × 500)

Pre-trained faster RCNN (Caffe framework)

1

Focused only on a single type of insulators and its fault

Jiang et al. [32]

UAV aerial images (China Power Grid)

485 (300 × 300)

Ensemble learning using SSD meta-arch (TF OD API framework)

1

Again, only a single-type faults was focused on using ensemble learning model

Tao et al. [33]

UAV captured images

1956; 996 synthethic (4608 × 3456)

Pre-trained ILN + DDN (similar mechanism with faster RCNN) (MXNet framework)

1

Single type fault and small-scale problem using a two-phase object detection (OD) method. Hence, it requires greater pre-processing of the input images

Bai et al. [35]

Picture manually from the industrial field

139 (227 × 227)

AlexNet + SPP-Net

1

A pre-trained model was utilized to transfer information and save time and computational resources. However, the model is limited to the image classification problem

Wang et al. [10]

Pictures achieved by industrial camera and mobile phone camera

510 (280 × 328)

Pre-trained CNN + object detection

1

The unique method described in this work successfully detects the power lines and its faults. However, the study explores just a unary EPTN component with

Guo et al. [37]

Helicopter captured images

250 (1024 × 1024)

RCF + FRAC

1

The dataset utilized in the model had low spatial, spectral, and geometric resolution. Consequently, the predictive model was not developed to characteristically identify broken strand faults at varying perception /distance-depth level

Chen et al. [38]

Images

300 (400 × 600)

SOFCN

1

This research proposes a fault detection approach employing a second-order full convolutional network (SOFCN) to overcome low-level visual feature extraction, background complexity, and classifier design methodology. This has resulted in the model complexity. A drawback to this method is the SOFCN approach requires segmented ground truth data, which are hard to annotate and limits the amount of EPTN component fault types that was considered

Liao et al. [39]

Aerial images

> 2000 (600 × 800)

Improved faster RCNN (Soft-NMS)

keras

1

The proposed method achieves great result for insulator fault types and suitable for detecting insulator defect in complex backgrounds. As a two/phase OD method, major demerit to this method is that it is quite slower than the normal Faster R-CNN

Li et al. [40]

UAV images

800

ROI mining Faster RCNN

The paper focuses on a unique fault type—bird’s nest obstruction detection. Also, the focus loss function is implemented in the RPN classification stage to balance the quantity of unbalanced data, foreground, and background data