A unified multi-scale deep convolutional neural network (MS-CNN) is proposed for fast object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network. The proposal sub-network performs detection at multiple output layers, allowing receptive fields to match objects of different scales. These complementary detectors are combined to produce a strong multi-scale object detector. The network is learned end-to-end using a multi-task loss. Feature upsampling via deconvolution is explored as an alternative to input upsampling, reducing memory and computation costs. The MS-CNN achieves state-of-the-art performance on datasets like KITTI and Caltech, with up to 15 fps. The proposal network generates object proposals by using multiple output layers, each focusing on different scale ranges. This approach allows the network to detect objects of various sizes effectively. The detection network uses ROI pooling and a deconvolution layer to enhance performance. The MS-CNN also incorporates context encoding and hard negative mining to improve detection accuracy. The MS-CNN outperforms existing methods in terms of detection speed and accuracy. It achieves high recall with a small number of proposals and performs well on both small and occluded objects. The network is efficient, with a detection speed of up to 15 fps on Caltech images. The MS-CNN is also effective on the Caltech pedestrian benchmark, achieving state-of-the-art performance. The network is trained using a multi-task loss function that combines classification and bounding box regression. The MS-CNN is a unified deep convolutional neural network that enables fast and accurate multi-scale object detection. It is efficient, with a detection speed of up to 15 fps, and achieves high recall with a small number of proposals. The network is effective on various datasets, including KITTI and Caltech, and outperforms existing methods in terms of detection speed and accuracy.A unified multi-scale deep convolutional neural network (MS-CNN) is proposed for fast object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network. The proposal sub-network performs detection at multiple output layers, allowing receptive fields to match objects of different scales. These complementary detectors are combined to produce a strong multi-scale object detector. The network is learned end-to-end using a multi-task loss. Feature upsampling via deconvolution is explored as an alternative to input upsampling, reducing memory and computation costs. The MS-CNN achieves state-of-the-art performance on datasets like KITTI and Caltech, with up to 15 fps. The proposal network generates object proposals by using multiple output layers, each focusing on different scale ranges. This approach allows the network to detect objects of various sizes effectively. The detection network uses ROI pooling and a deconvolution layer to enhance performance. The MS-CNN also incorporates context encoding and hard negative mining to improve detection accuracy. The MS-CNN outperforms existing methods in terms of detection speed and accuracy. It achieves high recall with a small number of proposals and performs well on both small and occluded objects. The network is efficient, with a detection speed of up to 15 fps on Caltech images. The MS-CNN is also effective on the Caltech pedestrian benchmark, achieving state-of-the-art performance. The network is trained using a multi-task loss function that combines classification and bounding box regression. The MS-CNN is a unified deep convolutional neural network that enables fast and accurate multi-scale object detection. It is efficient, with a detection speed of up to 15 fps, and achieves high recall with a small number of proposals. The network is effective on various datasets, including KITTI and Caltech, and outperforms existing methods in terms of detection speed and accuracy.