Bird Detection in the Field with the IA-Mask-RCNN

In recent times, field crop damage caused by birds, such as corvids and pigeons, has become crucial for many farmers. Damage can be as serious as the loss of a large part of the harvest. Several solutions have been proposed, but none are effective. An example is the use of scarecrows, but birds eventually adapt to them over time, and so they become ineffective. To study bird behavior and to propose a bird deterrent that would adapt to the presence of birds, we set up an experimental image-taking system on several plots of land over a period of 4–5 years. Around fifteen terabytes of images taken in the field were acquired. Our aim was to automatically detect these birds using deep learning methods and then to activate a real-time scarer. This work meets two challenges: the first is agroecological, as bird damage has become a major issue, and the second is IT, as it is difficult to detect birds in the field: the individuals are small because they are far from the camera lens, and field conditions are often less than optimal: darkness, confusion between the pigeons’ colors and the ground, etc. The Mask-RCNN in its original configuration is not suited to detecting small individuals. We mainly focused on the model’s hyperparameters to better adapt it to our study context. As a result, we improved the detection of small individuals using, among other things, appropriate anchor scales design and image processing techniques. At the same time, we built an original dataset focused on small individuals called BirdyDataset. The model can detect corvids and pigeons with an accuracy of 78% under real field conditions.