Table 1.
Comparison of deep learning algorithms for improving frame rate
| Categories | Method | Dataset | Result | Reference |
|---|---|---|---|---|
| Machine learning methods | Manual extraction of features | - | FPS: <20 | [14-23] |
| Early deep learning algorithms | CNN | - | FPS: <28 | [30-32] |
| Use of anchor-free detection algorithms | YOLO-OB | both | FPS: 39 | [36] |
| AFP-Net | Public | FPS: 52.6 | [38] | |
| Use of lightweight network architecture | Polyp-YOLOv5-Tiny | Public | FPS: 113.6 | [42] |
| YOLOv5m-TST | Public | FPS: 138.3 | [43] | |
| Use of algorithms based on one-stage detection methods | SSD based CNN | Public | FPS: 32 | [52] |
| SSDGPNet | Private | FPS: 50 | [53] | |
| MP-FSSD | Public | FPS: 62.5 | [54] | |
| YOLOv2 | Both | FPS: 67.16 | [50] | |
| YOLOv4 | Public | FPS: 122 | [51] |
CNN, convolutional neural network; YOLO, you only look once; YOLO-OB, you only look once-objectbox; AFP-Net, anchor-free polyp net; TST, token-sharing transformer; SSD, single shot multibox detector; SSDGPNet, single shot multibox detector for gastric polyps; MP-FSSD, multiscale pyramidal fusion single-shot multibox detector network.