In the first column, from (b) to (h), all methods except for M/D-YOLO v4 and YOLO v5 correctly identified the insulator string. However, compared to Pi-FT and Pi-Index, M/D-YOLO v3 had a larger localization error for insulator strings. Specifically, the right boundary of the predicted box by M-YOLO v3 was inaccurate, while the upper boundary of the predicted box by D-YOLO v3 exceeded the GTBox.
For the relatively small insulators, all methods correctly classified the insulators at positions (1), (3), and (10). The positions that are easily undetected or misclassified by these methods are (6), (7), and (11). According to the IDID’s annotations, M-YOLO v3 missed the insulators at positions (4), (6), and (7). D-YOLO v3 misclassified the “FlashDamaged” insulator at position (6) as “Good” and missed the insulators at positions (7) and (11). M-YOLO v4 missed the insulators at positions (6) and (7), and further misidentified the “Good” insulator at position (11) as “FlashDamaged”. D-YOLO v4 missed 3 insulators at positions (5), (7), and (8). YOLO v5 missed the insulator at position (9) and had false alarms at positions (8) and (11), in which the “Good” insulators were identified as “Good” and “FlashDamaged”. Both Pi-FT and Pi-Index detected the “Good” insulator at position (11) as both “Good” and “FlashDamaged”.
In summary, Pi-FT and Pi-Index were the most effective algorithms for labeled data. It is worth noting that the insulator at position (2) was not labeled in the IDID dataset, yet only Pi-Index was able to identify it. This demonstrates that Pi-Index is more effective for unlabeled data than other mainstream algorithms.
In the second column of Figure 9 (I), there is one insulator string containing 12 insulators. The insulators at positions (1) and (12) were only partially visible in the image, while the rest were entirely visible with clear edges. Specifically, positions (1)-(4), (6), and (8)-(12) were labeled as “Good”, whereas positions (5) and (7) were labeled as “Broken”.
The insulator string was identified only by D-YOLO v4, Pi-FT, and Pi-Index, whereas the rest of the methods missed it. Moreover, we focused on the detection details of the insulator string by D-YOLO v4, Pi-FT, and Pi-Index. D-YOLO v4 predicted a large error in locating the insulator string, and specifically its upper boundary exceeded that of the GT-Box. Pi-FT had false alarms on the prediction of the insulator string. The insulators at positions (1)-(5) and (3)-(12) were also misclassified as two separate insulator strings. However, Pi-Index has the best performance on the localization of the insulator strings. It correctly detected the insulators at positions (1)-(12) as one insulator string, and the boundary box basically overlaps with the GT-Box.
For the small-size insulators, all methods correctly detected the insulators at positions (2)-(4), (6), and (9)-(11). The differences of detection results from the above methods are mainly concentrated at positions (1), (5), (7), (8), and (12). Both M-YOLO v3 and D-YOLO v3 missed three insulators at positions (1), (5), and (12), but M-YOLO v3 further misidentified the “Good” insulator at position (7) as “Broken”. M/D-YOLO v4 missed the insulators at positions (1) and (12). Pi-FT missed the insulators at positions (1) and (8), while our Pi-Index only missed the insulator at position (1). YOLO v5 successfully detected all insulators, performing better than other mainstream methods. In summary, Pi-Index is significantly better than other mainstream algorithms except for YOLO v5.
When comparing Pi-Index and YOLO v5, we can be concluded that Pi-Index is significantly better than YOLO v5 in terms of the large-scale insulator string. However, Pi-Index has a higher miss rate than YOLOv5 in terms of the small-size insulators. Besides, Pi-Index outperforms YOLOv5 in terms of locating small targets. This is specifically reflected in the overlap rate between the predicted box and the GT-Box for the insulators at positions (5) and (7), where Pi-Index has a higher rate than that of YOLOv5.