  Previous research in $2D$ object detection focuses on various tasks,
including detecting objects in generic and camouflaged images. These works are
regarded as passive works for object detection as they take the input image as
is. However, convergence to global minima is not guaranteed to be optimal in
neural networks; therefore, we argue that the trained weights in the object
detector are not optimal. To rectify this problem, we propose a wrapper based
on proactive schemes, PrObeD, which enhances the performance of these object
detectors by learning a signal. PrObeD consists of an encoder-decoder
architecture, where the encoder network generates an image-dependent signal
termed templates to encrypt the input images, and the decoder recovers this
template from the encrypted images. We propose that learning the optimum
template results in an object detector with an improved detection performance.
The template acts as a mask to the input images to highlight semantics useful
for the object detector. Finetuning the object detector with these encrypted
images enhances the detection performance for both generic and camouflaged. Our
experiments on MS-COCO, CAMO, COD$10$K, and NC$4$K datasets show improvement
over different detectors after applying PrObeD. Our models/codes are available
at https://github.com/vishal3477/Proactive-Object-Detection.
