A Computer Vision-Based Attention Generator using DQN
Jordan Chipka
General Motors

Shuqing Zeng
General Motors

Thanura Elvitigala
General Motors

jbc274@cornell.edu

shuqing.zeng@gm.com

thanura.elvitigala@gm.com

Priyantha Mudalige
General Motors
priyantha.mudalige@gm.com

Abstract
A significant obstacle to achieving autonomous driving(AD) and advanced driver-assistance systems (ADAS) functionality in passenger vehicles is high-fidelity perception ata sufficiently low cost of computation and sensors. An areaof research that aims to address this challenge takes inspiration from human foveal vision by using attention-based
sensing. This work presents an end-to-end computer visionbased Deep Q-Network (DQN) technique that intelligently
selects a priority region of an image to place greater attention to achieve better perception performance. This method
is evaluated on the Berkeley Deep Drive (BDD) dataset. Results demonstrate that a substantial improvement in perception performance can be attained – compared to a baseline
method – at a minimal cost in terms of time and processing.

1. Introduction
As AD/ADAS features continue to advance, vehicles arebeing equipped with an increasing number of sensors andthe demand for high-definition (HD) sensing is likewise rising [6]. However, due to limited computing resources, thisdemand for HD sensing is often unworkable for real-timesystems [16]. Image-based object detection has a time complexity of O(N ). Therefore, this results in a tradeoff being
negotiated between perception performance and processingtime/power. While there is currently a large emphasis on
developing more powerful embedded computing platforms
for AVs, an alternative approach of addressing this difficulty
is to develop more intelligent methods of processing sensor
data.
A common technique is to mimic human foveal vision by
only processing select parts of the scene in HD when andwhere it is needed, rather than continually processing theentire scene [10, 1]. The most common method of multi-

resolution sensing employed in the automotive industry is
to use two front-facing cameras in tandem – one wide fieldof-view (FOV) low-resolution camera for close-range object detection and one narrow FOV high-resolution camera
for long-range object detection. A less expensive attention
tactic is to use one camera but process the full camera frame
at a downscaled resolution and process a small center cropof the frame at a higher resolution. This approach demands
a smaller pixel budget while allowing for HD detections in
the center of the frame to be obtained. However, these approaches have disadvantages in that they do not ensure thatthe HD imagery is being taken from the most relevant region of the image (e.g., when on curved roads, hills, intersections, etc.).
More sophisticated attention techniques typically fall
into two categories: bottom-up and top-down attention
models. A bottom-up attention model uses features or characteristics from the scene to derive its attention. A classic
illustration of bottom-up attention involves a human subject having their attention drawn to a single horizontal bar
in a scene filled with vertical bars [25]. Top-down attention
models, on the other hand, are based on prior knowledge
and are based on goals, expectations, and/or rewards. A
well-known example of top-down attention involves humansubjects watching a scene of a family when an unexpected
visitor enters into a room. Before watching the scene, thesubjects are given different tasks such as predicting the material circumstances of the family, estimating the ages ofthe people, and freely examining the scene. Based on theirgiven tasks, the human subjects demonstrate considerably
different eye movements [30].
Quickly and automatically detecting relevant areas of ascene through bottom-up attention models is an appealing
capability for machine vision [19]. Numerous studies inrecent years have used bottom-up attention models for thetasks of object segmentation, object recognition, image captioning, and visual question answering [8, 29, 24, 7]. TheseFigure 1. Workflow of the proposed attention-based sensing scheme.

methods typically use convolutional neural networks and/or
recurrent neural networks to identify the attention region –which is not favorable for safety-critical systems with realtime constraints due to additional computing needs. Another common bottom-up approach is to identify salient regions in the image [9, 2, 11]. However, for the applicationof autonomous driving, salient regions are not necessarily
the most relevant regions of the scene. Salient regions often
identify the most conspicuous objects in the scene, howeverthese objects do not require HD sensing as they could also
be detected with low resolution sensing. Rather, relevant regions in a roadway scene can include small distant objects
such as an upcoming traffic light or pedestrians crossing at
an upcoming crosswalk.
Although the majority of attention models use a bottomup approach, it is widely accepted that top-down factors
play a key role in attention guidance [12]. A recurrent attention model (RAM) and deep recurrent attention model(DRAM) were proposed to mimic human attention and havedemonstrated promising results for the tasks of image classification and digit recognition [20, 3]. Additional saliencybased techniques have also taken a top-down approach toderiving attention and predicting human gaze [13, 14, 28].
Finally, spatial transformers have been used as an attention
mechanism for digit classification tasks due to their advantage of being fully differentiable [15, 18].
Taking inspiration from recent advances in computer
vision-based reinforcement learning (RL), in particular,
Deep Q-Network (DQN) [17], as well as aspects from bothbottom-up and top-down attention models, we present alightweight DQN agent that intelligently selects the most
relevant region of the image to give more attention anddemonstrate its benefit on the task of object detection on
roadway scenes. This approach is a hybrid between bottomup and top-down attention because it uses low-level visualfeatures as its input, but is trained to achieve a high-levelgoal through the DQN agent’s reward function. This DQN
agent emulates human foveal vision by using low-resolution
for peripheries but high-resolution for critical areas, andtherefore reduces the overall number of pixels to process.

This approach offers a more optimized technique to address
the tradeoff between efficiency and effectiveness. Finally,
this method is self-contained, derived only from perception,and therefore can be easily integrated into any existing perception system.

2. Methodology
This work aims to develop an intelligent attentionsubsampling technique in which the required compute resources are significantly reduced while the perception performance is retained. Figure 1 shows the workflow for the
proposed technique in which human foveal vision is emulated as a bi-directional process. In this workflow, an HD
image is captured from the camera stream and passed on to
the attention-based subsampling mechanism. The subsampling mechanism first downscales the full camera frame to a
standard-definition (SD) resolution and then passes it along
to the DQN-based attention module. This module then identifies the most relevant region of the image and passes thatback to the attention-based subsampling module in the formof an attention signal. Based on the attention signal, the subsampling mechanism crops out an attention window fromthe original HD frame and passes that on to the perception
module. Finally, object detection is performed on both the
SD full-frame and the HD attention window, and the resulting mixed-resolution detections are fused to yield the final
objects.

2.1. DQN-Based Attention Agent
The first step to developing the DQN-based attention
agent is establishing its action space. The action, A∗t , ofthe attention agent is simply the selected location of the attention window’s centroid (i.e., x and y locations in image
coordinates). To simplify the action space, we discretize theimage into Nx × Ny blocks. Then, a RNx × RNy attention
window is determined based on the given action, A∗t . Figure 2 depicts the discretized image and the attention agent’s
action space.
The attention/perception module shown in Figure 1

mum value of this output represents the predicted row for
the attention window’s centroid that would maximize thereward.

2.2. Training Process

Figure 2. Depiction of the discretized image, attention region(blue), centroid of attention region (orange), and action space(red). The action space does not encompass the entire image inorder to ensure the attention region never extends beyond the image boundary.

is mainly comprised of a CNN-based object detection(OD) network, as well as a lightweight deep Q-network(DQN) [4]. Figure 3 shows a detailed view of the attention/perception module and how it fits into the overall workflow. Object detection is performed twice sequentially –once for the SD frame and then once for the HD attention
frame. First, the SD full-frame is passed into the OD network and its detections are then received out from the OD
head. YOLOv5, the latest version in the YOLO object detector family, was used in this study [21, 22, 23, 5, 26].
Before being passed to the OD head, the resulting featuretensor from the CNN backbone is intercepted to be used as
the input to the DQN. The output of the DQN is an arrayof Q-values, which for our application, represents the anticipated reward for each possible location of the attention
window, At . The argmax of this array is then taken to obtain the attention signal, A∗t . Finally, based on this attention
signal, an HD attention frame is cropped from the original
HD image, passed to the OD network, and HD attention
frame detections are then received and fused with the SD
full-frame detections to yield the final objects.
The architecture of the DQN consists of two branches,both of which are fully connected neural networks comprised of one hidden layer. One branch is used to select thex location of the attention window, while the other branch
is used to select the y location of the attention window. The
architecture of the DQN is depicted in Figure 4. The input to the DQN is the feature tensor from the object detection backbone. This feature tensor is then flattened andpassed to two distinct, fully connected networks. The first
network outputs an array with size Nx . This output’s maximum value represents the predicted column for the attention
window’s centroid that would maximize the reward. The
second network outputs an array with size Ny . The maxi-This work uses standard techniques for training the
DQN but with a few minor adjustments. The DQN is
trained to approximate a function that can predict Q-values,
which are a measure of the agent’s expected reward, given
an action At , state Xt , and network parameters θ. The
policy which governs the agent’s actions, πθ (At , Xt−1 ),is an epsilon greedy policy around the function A∗t =arg maxAt Q(At ) [27]. This policy is used to improvetraining stability and increase the likelihood of convergence.
The DQN was trained using the Berkeley Deep Drive(BDD) dataset [31]. The images contained in the BDD
dataset are not sequential, and hence each image could beconsidered independent from the next. Therefore, a replay
memory was not needed to randomly sample data in orderto ensure decorrelated batches. Also, as a result of dealing
with temporally independent data, the discount factor, γ,
was set to be 0. The discount factor is a constant between
0 and 1 that determines how the agent is rewarded – i.e., a
discount factor of 0 will reward the agent simply based on
the instantaneous reward, while a value closer to 1 will give
more significance to expected rewards in the future. Since
our DQN essentially operates on a frame-by-frame basis,
our agent is only concerned with maximizing the reward
for the current frame, and therefore, the discount factor isset to 0.
2.2.1

Loss Function

With a discount factor of 0, the original training rule (asshown in Eq. 1) simplifies to Eq. 2.
  \label {eqn: original training rule} Q^\pi (X,A) = r + \gamma Q^\pi (X', \pi (X')) 

(1)

  \label {eqn: simplified training rule} Q^\pi (X,A) = r (2)  \label {eqn: error} \delta = |Q^\pi (X,A) - r| (3)

In the above equations, Qπ (X, A) are the Q-values according to policy π, given state X and action A. The reward
is represented as r, and the expected future state is X ′ . The
difference between the two sides of the equality in Eq. 2 isthe error, δ, that we are trying to minimize during training(as shown in Eq. 3). Since the DQN has two branches, one
for the attention window’s x position and the other for its
y position, the network produces two sets of Q-values forFigure 3. Detailed view of the attention/perception module and how it fits in the overall workflow.

was 0. Finally, if applying the attention window resulted inless true positive detections, then the reward was -1. This
reward function is summarized in Eq. 5.

  \label {eqn: reward} r = \left \{ \begin {array}{ll} -1 & TP' - TP < 0 \\ 0 & TP' - TP = 0 \\ 1 & TP' - TP > 0 \end {array} \right . 

Figure 4. The network architecture of the DQN consists of two
fully connected branches – one to predict the x location of theattention window and another to predict the y location of the attention window.

each batch during training. Therefore, we get two error values, δx and δy . Using an L1 loss function, we show the final
training loss in Eq. 4, where B is the batch size.
  \label {eqn: loss} \mathcal {L} = \sum _{i=0}^{B} (\delta _x + \delta _y) 

2.2.2

(4)

Reward Function

A simple reward function was implemented to train the
DQN agent. This reward function compared the number oftrue positive detections before applying the attention window, T P , to the number of true positive detections after applying the attention window, T P ′ . If applying the attention
window resulted in more true positive detections, then the
reward was +1. If applying the attention window resulted in
the same amount of true positive detections, then the reward(5)

For this study, the reward function was designed in a way
to simply maximize true positive detections of any object.
As will be shown in section 3 of this paper, the DQN agent
learned to seek out small, distant vehicles using its attention window. This is due to vehicles being the dominantclass in the BDD dataset. However, a more sophisticatedreward function can easily be tailored in order to give moresignificance to other classes that may be of more interest.
Furthermore, additional post-processing techniques could
be implemented to give more significance to other regions
based on other sources of information, such as a map, path
planning, etc.

3. Results
3.1. Experimental Setup
The DQN-based attention mechanism was tested on bothsequential and non-sequential data. For non-sequential
data, only one camera frame could be used throughout theattention workflow. In other words, the attention window
is applied to the same camera frame that provided the input
feature tensor to its DQN. However, for sequential data, thisprocess spans two frames so that the camera frame at time
t is used to generate the attention window that is applied tothe camera frame at time t + 1.
The experimental setup consists of comparing the performance of YOLOv5 at four different image resolutions.
The first resolution is the original HD image resolution of
0.92 MP. Second, a downscaled SD image resolution of 0.25

Figure 5. Summary of the experimental setup.

MP is used as the baseline. The third resolution is a further downscaled resolution of 0.11 MP. Finally, a 0.11 MP
HD attention crop of the original image, referred to as theregion of interest (ROI), is combined with the previously
mentioned 0.11 MP downscaled frame to yield a 0.22 MP
attention frame. A summary of this experimental setup is
depicted in Figure 5.

3.2. Evaluation
Average precision (AP) was used as the performance
metric for this study. Inference time was also used as asecondary performance metric. In addition to the DQNbased attention model, two additional attention models wereused for comparison. First, a centered attention model wastested, which always placed the attention window in the
middle of the camera frame. Second, a random attention
model was tested, which randomly placed the attention window within the camera frame. Four classes were used during the evaluation – vehicle, traffic light, pedestrian, and bicycle. Finally, a weighted mean average precision (wMAP)
was used to calculate the average precision over all classes,
according to their prevalence in the dataset. Figures 6-10
show the wMAP and AP for various classes as a functionof intersection over union (IOU). The inference time of thevarious methods is shown in Figure 11.

Figure 6. Weighted mean average precision as a function of IOU
for the BDD dataset.

4. Discussion
4.1. Impact on Inference TimeThe results in the previous section demonstrate that the
proposed attention mechanism can provide a considerable
performance boost in terms of average precision while only
requiring a small increase in processing time, as shown in
Figure 11. Although the total amount of pixels being processed for the attention method is fewer than for the baseline method (0.22 MP vs. 0.25 MP), the attention method
requires YOLOv5 to be run twice to provide the detections
for the low-resolution frame, as well as the region of in-Figure 7. Average precision for vehicles as a function of IOU for
the BDD dataset.

Figure 8. Average precision for traffic lights as a function of IOU
for the BDD dataset.

Figure 10. Average precision for bicycles as a function of IOU for
the BDD dataset.

Figure 11. Inference time of YOLOv5 object detector at various
resolution levels.

a slightly higher inference time; however, it is still much
faster (less than half the required processing time) than thefull resolution (HD) method. To further reduce processing
time, a more sophisticated CNN architecture could be developed to process both frames simultaneously.

4.2. Impact on Detection Accuracy

Figure 9. Average precision for pedestrians as a function of IOU
for the BDD dataset.

terest (ROI) frame. Therefore, the extra overhead involved
with running the object detection network twice results in

As shown in Figure 7, the attention method provides aconsiderable improvement in performance over the baseline method for the vehicle class. The difference between
the baseline results and the full resolution (HD) results –which represent the upper bound that can be achieved withthe given data and object detection network – is nearly cut
in half when the attention mechanism is used. However,the performance boost that the attention mechanism provides for wMAP is not as considerable. This is due to thelower performance of the attention method for the traffic
light class – which performed comparably to the baseline
method – and the pedestrian and bicycle classes – which
both performed worse than the baseline method. AlthoughFigure 12. Illustration of evaluation results using BDD data. Notice the two distant pedestrians crossing the street, as well as the bus andparked cars along the side of the street, that were detected in the attention window, but not in the baseline view.

the vehicle class is the dominant class in the BDD dataset,the weaker performance seen in the other three classes is
the reason for the less considerable performance boost seen
overall in Figure 6.
The attention mechanism provided improved performance for vehicles because the DQN agent learned to place
the attention window in locations that were likely to contain small, distant vehicles (i.e., near the center of the image
along the horizon line). While traffic lights often appear inthis region of the image, they also frequently appear above
the horizon line, as well as along the left/right side of theframe when approaching intersections. Therefore, a significant performance boost is not seen for traffic lights. Similarly, bicycles and pedestrians do not often appear nearby
small and distant vehicles but rather are often found on
the left/right side of the frame on sidewalks. Therefore,
the attention method produces diminished results for theseclasses. However, due to the dominance of vehicles in the
BDD dataset, the overall performance (wMAP) of the attention method still outperforms the baseline despite using
fewer pixels overall.
As mentioned previously in section 2.2.2, the proposed
methodology is highly flexible and can be modified to account for different objectives. While the DQN trained forthis study simply attempted to maximize true positive de-tections, the reward function can be crafted to give moresignificance to other classes. Furthermore, other sourcesof information, such as map and path planning data, canbe incorporated to allow for dynamic objectives. For example, if the AV’s on-board map indicates that the vehicle
is approaching a traffic light, then it can employ a DQN
agent trained specifically to detect traffic lights. However,if the map indicates that the vehicle is approaching a crosswalk, then the AV can employ a different DQN agent trained
specifically to detect pedestrians in the AV’s intended path.
Incorporating dynamic objectives such as these allows themost appropriate region of interest to be identified according to the most relevant task for the AV at the moment.
As shown in Figures 6-10, the DQN-based attention scheme (dark blue) outperformed the other attention
schemes. Centered attention (green) also performed favorably, considering that small, distant vehicles are often foundnear the center of the frame. However, it still did not perform as well as the DQN-based attention scheme, therefore,
indicating that the DQN agent is, in fact, able to intelligently
seek out additional objects. Random attention (light blue),on the other hand, did not perform favorably. It only slightly
outperformed the low-resolution detections because, while
it does provide additional resolution, there is no intelligencebehind the placement of the attention window, and so theFigure 13. Illustration of the tradeoff between higher detection accuracy within attention window and lower detection accuracy outside ofattention window.

additional resolution often goes to waste.

4.3. Illustrative Example
An illustration of the detections received for the various
methods is shown in Figure 12. In this figure, the HD 0.92
MP frame is shown in the bottom right quadrant, along withits detections (red for vehicles, blue for pedestrians). The
baseline SD 0.25 MP frame is shown in the bottom left
quadrant, along with its detections. The upper right quadrant shows the 0.11 MP region of interest (ROI) and its detections. Finally, the upper left quadrant shows the locationof the attention window (white rectangle), along with the
fused attention detections, which come from the 0.11 MP
ROI window (top right) and the 0.11 MP low-resolution
full-frame (not shown). This snapshot demonstrates how
the attention mechanism provides many more small, distantdetections compared to the baseline.
While the attention window gives higher detection accuracy within the region of interest, detection performanceoutside of the region of interest is diminished. This is expected as the proposed attention-based sensing paradigm
mimics human foveal vision by providing high-resolution
in one specific area, while the peripheral region is processed
at a lower resolution. This tradeoff is illustrated in 13, as we
see numerous vehicle and traffic light detections in the at-tention window, but also some missed detections outside of
the region of interest along the left side of the frame. However, due to the intelligent nature in which the DQN agent
places the attention window, and its ability to fixate on themost relevant region of the scene, the positive effects of thistradeoff far outweigh the negative effects.

5. Conclusion
This paper presents a novel attention-based sensing technique that leverages an end-to-end DQN-based agent to intelligently select a region of an image to place greater attention to achieve better perception performance. Due tothe flexibility of the proposed method, the DQN agent canbe crafted to maximize performance for various objectives.
This technique presents several advantages for AD/ADAS
applications, such as requiring fewer sensors, lower cost,less processing time and power consumption, improved perception capability, and enhanced HD sensors utilization.
Overall, a substantial improvement in perception capability
at a small cost in terms of processing time was demonstratedusing the Berkeley Deep Drive dataset.

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