<p>Your model needs to be trained with negative examples (i.e. images without any cars in them). It needs to understand that it is possible that an image doesn't have any car.</p>
<p>Further, it looks like the model has understood some unrelated features as car features. E.g.: the parking lot area. To avoid this use random erasing augmentation while training model.</p>
<p><a href="https://arxiv.org/abs/1708.04896" rel="nofollow noreferrer">Random erasing</a> -Inspired by the mechanisms of dropout regularization, random erasing can be seen as analogous to dropout except in the input data space rather than embedded into the network architecture. By removing certain input patches, the model is forced to find other descriptive characteristics.</p>
TensorFlow False Detection
<p>I have trained TensorFlow model with 200 car images, they can success detection:</p>
<p><a href="https://i.stack.imgur.com/jFXsS.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/jFXsS.png" alt="enter image description here" /></a></p>
<p>But, when there is no car in the image, a false car detection occurs, why?, how i can prevent this?:</p>
<p><a href="https://i.stack.imgur.com/9KWsC.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/9KWsC.png" alt="enter image description here" /></a></p>