Detection Results:
Yolov4 to the Left and Yolov5 to the Right
YoloV4 and V5 models are trained and tested under consistent conditions of augmentations and pre-processing.
Mangos are annotated in their respective formats which can be accessed from YoloV4 augmented Dataset and YoloV5 augmented Dataset folders.
- Auto-orientation of pixel data
- Resizing to 416x416
- 50% probability of horizontal flip
- 50% probability of vertical flip
- Random rotation of between -15 and +15 degrees
- Random shear of between -15° to +15° horizontally and -15° to +15° vertically
- Random brigthness adjustment of between 0 and +85 percent
- Random exposure adjustment of between -27 and +27 percent
- Salt and pepper noise was applied to 1 percent of pixels
Here are the results obtained with their best weights.
| Parameters | Yolov4(6000 epochs) | Yolov5(300 epochs) | Yolov4-tiny(6000 epochs) | Detectron 2 | Faster RCNN |
|---|---|---|---|---|---|
| Precision | 0.76 | 0.5676 | 0.64 | ||
| Recall | 0.83 | 0.8427 | 0.68 | ||
| F1-score | 0.80 | 0.678 | 0.66 | ||
| mAP@0.5 | 0.82 | 0.791 | 0.6146 |
| Title | Architectures Used | Data Augmentation Techniques | Evaluation of Detection Performance | Graphs Plotted | Results |
|---|---|---|---|---|---|
| Deep Fruit Detection in Orchards | Faster R-CNN | Image Flipping and Rescaling | Tiling approach FR-CNN | Average Precision Response Area under Precision Recall Curve, F1-score Avg.Precision vs Number of Training images | F1 score > 0.9; Precision = 0.958; Recall = 0.863 |
| Fast implementation of real-time fruit detection in apple orchards usingdeep learning | LedNet | Two level scale amplification | Average Precision Response, IoU, Precision, Recall | Focal Loss and MSE functions vs Object confidence Score | Recall = 0.821; Accuracy = 0.853 |
