Recent CNN-based object detection methods have drastically improved their performances but still use a single classifier as opposed to “multiple experts” in categorizing objects. The main motivation of introducing multi-experts is twofold: i) to allow different experts to specialize in different fundamental object shape priors and ii) to better capture the appearance variations caused by different poses and viewing angles. The proposed approach, referred to as multi-expert Region-based CNN (ME R-CNN), consists of three experts each responsible for objects with particular shapes: horizontally elongated, square-like, and vertically elongated. Each expert is a network with multiple fully connected layers and all the experts are preceded by a shared network which consists of multiple convolutional layers. On top of using selective search which provides a compact, yet effective set of region of interests (RoIs) for object detection, we augmented the set by also employing the exhaustive search for training. Incorporating the exhaustive search can provide complementary advantages: i) it captures the multitude of neighboring RoIs missed by the selective search, and thus ii) provide significantly larger amount of training examples to achieve the enhanced accuracy.