I have searched through this forum for similar questions but was unanswered (Updating Tensorflow Object detection model with new images). I have managed to create my custom train model (lets name it model1). Was wondering if can i use new images that are processed by model1 to further train model1? will it improve the accuracy of the model?
The main problem of machine learning (that is the approach you are using for object detection here) is that of generalization. In your case, it is the ability to recognize objects of the same type as image you used for training, in images that were not used during training.
Obviously, if you were able to use all the possible images during training, your system would be perfect (actually, it would be a simple exact image matching problem). In a more realistic setup, the more training image you are using, the higher chance you have to obtain a better object detector.
Usually, it is however more valuable to add hard examples to your training set. Hence, if your application allows it (in terms of computation time in particular) you can indeed add all the images that are wrongly detected in your dataset (with the correct label) and it will probably help to get a better model, able to detect the object in harder condition on new images.
However, it really depends on what you are doing. If you want to compare your system to another one, you need to use the same (training and) test images to be fair. For benchmarking, you are not allowed to include test images in the training dataset! When you compute the accuracy (on a validation/test dataset) to compare several settings, be sure you are fair in this comparison.