{"id":872,"date":"2023-06-01T17:09:06","date_gmt":"2023-06-01T17:09:06","guid":{"rendered":"https:\/\/ai-box.eu\/?p=872"},"modified":"2023-06-01T17:15:04","modified_gmt":"2023-06-01T17:15:04","slug":"yolov5-optimization-of-training-data-for-pfm-1-antipersonnel-mine-detection","status":"publish","type":"post","link":"https:\/\/ai-box.eu\/en\/ai-pipeline-en\/yolov5-optimization-of-training-data-for-pfm-1-antipersonnel-mine-detection\/872\/","title":{"rendered":"YOLOv5 – Optimization of training data for PFM-1 antipersonnel mine detection"},"content":{"rendered":"

After training the first 31 models for anti-personnel mine detection, my data set has grown significantly. The goal was to reduce the false positives. This was because there were always leaves detected that were similar to a PFM-1 anti-personnel mine and were falsely detected as such. In order for the YOLOv5 network to learn that leaves should not be detected as mines, a larger number of images of grass, trees, bushes, raspberry plantations etc. were mixed into the training data. Furthermore, a dataset showing synthetic images of PFM-1 mines from a drone’s point of view was mixed in. Thus, thanks to this dataset, the perspective could be extended to the top-down view of a drone on PFM-1 mines. Furthermore, existing image information was cleaned and enriched to reduce typical features of the PFM-1 mine that had become too prominent to allow for more general detection. It is normal that the PFM-1 mine can only be recognized in parts and still has to be recognized. If then a characteristic predominates with the recognition which can be possibly covered leads to a not recognizing of the mine.<\/p>\n

In summary, the data generated in this way has had great success in optimizing the training data and significantly improving the results.<\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n