  Machine learning has become a major field of research in order to handle more
and more complex image detection problems. Among the existing state-of-the-art
CNN models, in this paper a region-based, fully convolutional network, for fast
and accurate object detection has been proposed based on the experimental
results. Among the region based networks, ResNet is regarded as the most recent
CNN architecture which has obtained the best results at ImageNet Large-Scale
Visual Recognition Challenge (ILSVRC) in 2015. Deep residual networks (ResNets)
can make the training process faster and attain more accuracy compared to their
equivalent conventional neural networks. Being motivated with such unique
attributes of ResNet, this paper evaluates the performance of fine-tuned ResNet
for object classification of our weeds dataset. The dataset of farm land weeds
detection is insufficient to train such deep CNN models. To overcome this
shortcoming, we perform dropout techniques along with deep residual network for
reducing over-fitting problem as well as applying data augmentation with the
proposed ResNet to achieve a significant outperforming result from our weeds
dataset. We achieved better object detection performance with Region-based
Fully Convolutional Networks (R-FCN) technique which is latched with our
proposed ResNet-101.
