A camera comprising: a housing through which extends an aperture that permits light to enter the housing; an image sensor contained within the housing to receive the light that has entered the housing through the aperture; a processor communicatively coupled to the image sensor; and a memory communicatively coupled to the processor, wherein the memory has stored thereon computer program code that is executable by the processor and that, when executed by the processor, causes the processor to perform a method comprising: obtaining an image; generating a first feature pyramid by processing the image using a convolutional neural network, CNN; generating a second feature pyramid from the first feature pyramid using the CNN, wherein the second feature pyramid comprises an enhanced feature map generated by combining an upsampled feature map and a feature map of the first feature pyramid that has a corresponding or lower resolution of a resolution of the enhanced feature map, and wherein the upsampled feature map is generated by upsampling a feature map of the second feature pyramid that is at a shallower position in the CNN than the enhanced feature map; splitting the enhanced feature map into channel feature maps of different resolutions, wherein each of the channel feature maps corresponds to channels of the enhanced feature map; and performing object detection on the channel feature maps, wherein a last feature map of the first feature pyramid is used as a first feature map of the second feature pyramid, and is input to first de-convolutional layers of the second feature pyramid. a housing through which extends an aperture that permits light to enter the housing; an image sensor contained within the housing to receive the light that has entered the housing through the aperture; a processor communicatively coupled to the image sensor; and a memory communicatively coupled to the processor, wherein the memory has stored thereon computer program code that is executable by the processor and that, when executed by the processor, causes the processor to perform a method comprising: obtaining an image; generating a first feature pyramid by processing the image using a convolutional neural network, CNN; generating a second feature pyramid from the first feature pyramid using the CNN, wherein the second feature pyramid comprises an enhanced feature map generated by combining an upsampled feature map and a feature map of the first feature pyramid that has a corresponding or lower resolution of a resolution of the enhanced feature map, and wherein the upsampled feature map is generated by upsampling a feature map of the second feature pyramid that is at a shallower position in the CNN than the enhanced feature map; splitting the enhanced feature map into channel feature maps of different resolutions, wherein each of the channel feature maps corresponds to channels of the enhanced feature map; and performing object detection on the channel feature maps, wherein a last feature map of the first feature pyramid is used as a first feature map of the second feature pyramid, and is input to first de-convolutional layers of the second feature pyramid. obtaining an image; generating a first feature pyramid by processing the image using a convolutional neural network, CNN; generating a second feature pyramid from the first feature pyramid using the CNN, wherein the second feature pyramid comprises an enhanced feature map generated by combining an upsampled feature map and a feature map of the first feature pyramid that has a corresponding or lower resolution of a resolution of the enhanced feature map, and wherein the upsampled feature map is generated by upsampling a feature map of the second feature pyramid that is at a shallower position in the CNN than the enhanced feature map; splitting the enhanced feature map into channel feature maps of different resolutions, wherein each of the channel feature maps corresponds to channels of the enhanced feature map; and performing object detection on the channel feature maps, wherein a last feature map of the first feature pyramid is used as a first feature map of the second feature pyramid, and is input to first de-convolutional layers of the second feature pyramid.